# embeddings_queue

created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 746022e3-9107-4555-a708-254909164284
encoding: FLOAT32
metadata:
{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","page_label":"15","author":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"chroma:document":"2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization\nNatural language interfaces have contributed to generating visualizations\ndirectly from natural language, but visualization problems remain challenging\ndue to the ambiguity of natural language.ChatGPT provides a new avenue for\nthe ﬁeld by converting natural language into visualized code.\nIn terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-\nsic arithmetic skills by asking questions.On the iris dataset, Titanic survival\ndataset, Boston housing data, and randomly generated insurance claims dataset,\nthe statistical analysis of data and visualization problems were converted to\nprogramming problems using Jupyter to verify ChatGPT’s ability to generate\npython code to draw suitable graphs and analyze the data. The results show\nthat ChatGPT can access structured and organized datasets to perform the\nfour basic software operations required for databases: create, read, update, and","page":14}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ffc1c95e-e2e3-4c6c-bb5f-cdf85dbb7fb7
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page":14,"moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","subject":"","author":"","total_pages":35,"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","page_label":"15","chroma:document":"four basic software operations required for databases: create, read, update, and\ndelete, and generate suitable python code to plot graphs for descriptive statis-\ntics, variable correlation analysis, describing trends, and other data analysis\noperations.Maddigan et al. [47] proposed an end-to-end solution for visualizing\ndata in natural language using LLM, which uses an open-source python frame-\nwork designed to generate appropriate hints for selected datasets to make LLM\nmore eﬀective in understanding natural language, and uses internal reasoning\ncapabilities to select the appropriate visualization type to generate the code for\nvisualization. In this paper,the reseachers compare the visualization results of\nGPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and\nthe visualization results of energy production dataset in the study of ADVISor\nwith NL4DV [49, 50].In addition to, they explore the ability to reason and hy-"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0c9bdc57-6a8a-47b6-af05-0f35f2168a38
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{"author":"","producer":"pdfTeX-1.40.21","page":14,"page_label":"15","subject":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-\npothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or\nwrong .Experimental results show that LLM can eﬀectively support the end-to-\nend generation of visualization results from natural language when supported\nby hints, providing an eﬃcient, reliable and accurate solution to the natural\nlanguage visualization problem.\n15","creator":"LaTeX with hyperref","trapped":"/False","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 197d0e6d-0c57-4023-8dd1-65bc5e5aa6af
encoding: FLOAT32
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{"trapped":"/False","title":"","page_label":"16","chroma:document":"Information Extraction\nThe goal of information extraction is to extract speciﬁc information from\nnatural language text for structured representation, including three important\nsubtasks such as entity relationship extraction, named entity recognition, and\nevent extraction, which have wide applications in business, medical, and other\nﬁelds.\nIn information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-\nbased multi-round question-and-answer framework for information extraction.\nThe framework decomposes a complex information extraction (IE) task into\nseveral parts, then combines the results of each round into a ﬁnal structured\nresult. The entity association triple extraction, named entity recognition, and\nevent extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0\n, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,\ncomparing three metrics of precision, recall, and F1 score.These results sug-","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":15,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","producer":"pdfTeX-1.40.21","subject":"","keywords":"","total_pages":35}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 5d289831-44f3-48df-86aa-88caf79d8ea7
encoding: FLOAT32
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{"keywords":"","title":"","creator":"LaTeX with hyperref","trapped":"/False","subject":"","page_label":"16","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":15,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"chroma:document":"comparing three metrics of precision, recall, and F1 score.These results sug-\ngest that on six widely used IE datasets, ChatIE improves performance by an\naverage of 18.98% compared to the original ChatGPT without ChatIE, and out-\nperforms the supervised models FCM and MultiR [57, 58] on the NYT11-HRL\ndataset.While the original ChatGPT cannot solve complex IE problems with\noriginal task instructions, and with this framework, successfully IE tasks were\nimplemented on six datasets.Gao et al. [59] explored the feasibility and chal-\nlenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the\nperformance of ChatGPT in long-tail and complex scenarios (texts containing\nmultiple events) and comparing it with two task-speciﬁc models, Text2Event\nand EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-\nmance of ChatGPT. The results show that the average performance of Chat-\nGPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0de7422f-446f-4ef8-b31b-6c9db863d67e
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc\nmodels such as EEQA. Continuous reﬁnement of cues does not lead to consis-\ntent performance improvements, and ChatGPT is highly sensitive to diﬀerent\ncue styles.Tang et al. [62] proposed a new training paradigm that incorporates\nappropriate cues to guide ChatGPT to generate a variety of examples with dif-\nferent sentence structures and language patterns and eliminate the resulting\nlow-quality or duplicate samples for downstream tasks. Although compared to\na soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named\nEntity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene\nAssociation Database (GAD) Release; EU-ADR corpus for the RE task , the\ninnovative training framework was able to train local models, with F1 scores im-\nproving from 23.37% to 63.99% for the named entity recognition task and from","author":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","subject":"","page":15,"page_label":"16","total_pages":35}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 77002706-bec2-4235-9c61-3dababae5ed7
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","page_label":"16","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","title":"","chroma:document":"proving from 23.37% to 63.99% for the named entity recognition task and from\n75%, while alleviating privacy concerns and time-consuming data collection and\nannotation problems.He et al. [63] proposed a contextual learning framework\nICL- D3IE. this framework introduces formatted presentation, continuously it-\nerates to update and improve the presentation, and then combines ChatGPT\nfor text information extraction. In the paper, ICL-D3IE is compared with ex-\nisting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and\nout-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE\n[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and\nsettings except for the ID setting on CORD are superior to other methods,\n16","trapped":"/False","author":"","keywords":"","total_pages":35,"page":15}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 7567faed-662b-4ff3-a244-841235153d2e
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","page":16,"author":"","page_label":"17","moddate":"2023-04-05T00:33:07+00:00","title":"","producer":"pdfTeX-1.40.21","chroma:document":"with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on\nSROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much\nbetter than previous pre-trained methods on all datasets.Polak et al. [67] pro-\nposed ChatExtract method - consisting of a set of engineering prompts applied\nto a conversational LLM - for automatic data extraction. In the experiment,\nthey extracted a large number of sentences from hundreds of papers and ran-\ndomly selected 100 sentences containing data and 100 sentences without data\nas test data. The results show that the accuracy and recall of LLM exceeded\n90% and may be comparable to human accuracy in many cases; in addition to\nthis, the experiments were conducted under the condition of removing follow-up\nprompts and not keeping the conversation compared to previous experiments,\nrespectively. The accuracy of deleting follow-up questions dropped to 80.2%\nand the recall rate dropped to 88.0%. Removing the conversational aspect and","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 148ae4b2-4049-450b-8890-1d157cfff05a
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{"chroma:document":"and the recall rate dropped to 88.0%. Removing the conversational aspect and\nrelated information retention recall and accuracy dropped to 90.0% and 56.6%,\nrespectively, demonstrating the eﬀect of information retention combined with\npurposeful redundancy on LLM information extraction performance.\nQuality Assessment\nFor translation quality, text generation quality, manual assessment is usually\neﬀective but suﬀers from subjectivity and time-consuming, etc. It was found\nthrough exploration that ChatGPT has also achieved signiﬁcant performance\nin automatic quality assessment.\nIn terms of quality assessment,Kocmi et al. [68] proposed a GPT-based\ntranslation quality assessment metric, GEMBA, which evaluates the transla-\ntion of each fragment individually and then averages all the obtained scores to\nobtain a ﬁnal system-level score. In the MQM2022 test set (English-German,\nEnglish-Russian, and Chinese-English) [69], a scoring task was performed with","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":16,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page_label":"17","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 54b68a65-208f-4c5a-804a-6a72bf03005b
encoding: FLOAT32
metadata:
{"subject":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"17","author":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","page":16,"chroma:document":"English-Russian, and Chinese-English) [69], a scoring task was performed with\na classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of\nseven GPT models under four cue templates.The results showed that GEMBA\nhad the highest system-level accuracy of 88.0% compared to more than 10 au-\ntomatic metrics such as BLEU, and among the seven GPT models, ChatGPT\naccuracy is above 80%, in addition to, the best performance can be obtained in\nthe least constrained template, demonstrating the potential of LLM for trans-\nlation quality assessment tasks, but the evaluation is only applicable at the\nsystem level and needs further improvement.Wang et al. [72] used ChatGPT\nas a natural language generation (NLG) evaluator to study the correlation with\nhuman judgment. On three datasets covering diﬀerent NLG tasks, task- and\naspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in\nCNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d7c00460-5996-4185-a59c-1af3a3188702
encoding: FLOAT32
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{"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","title":"","page":16,"page_label":"17","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,\nand data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-\ncients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-\nsess the correlation with human evaluations.The results show that ChatGPT is\nhighly correlated with human judgments in all aspects, with correlation coeﬃ-\ncients of 0.4 or more in all categories, showing its potential as an NLG indicator.\n17","subject":"","keywords":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ff9ac3c7-f1a8-4d1b-a8bf-7acb5537738f
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{"page":17,"keywords":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Data Augmentation\nIn natural language processing, text data augmentation is an eﬀective mea-\nsure to alleviate the problem of low data quantity and low quality training data,\nand ChatGPT has shown great potential in this regard.\nIn terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based\ntext data augmentation method that reformulates each sentence in the train-\ning sample into multiple conceptually similar but semantically diﬀerent samples\nfor classiﬁcation tasks downstream of the Bert model.On text transcriptions\nand PubMed 20k datasets containing more than 8 hours of audio data of com-\nmon medical symptom descriptions,experiments were conducted to compare co-\nsine similarity and TransRate metrics with multiple data enhancement methods\n[9].This paper shows that compared with existing data enhancement methods,\nthe proposed ChatAug method shows a double-digit improvement in sentence\nclassiﬁcation accuracy and generates more diverse augmented samples while","page_label":"18","creator":"LaTeX with hyperref","author":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9e93394b-95f6-4067-8be9-cf5e501374fd
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","chroma:document":"classiﬁcation accuracy and generates more diverse augmented samples while\nmaintaining its accuracy, but the original model is not ﬁne-tuned in the pa-\nper and suﬀers from a lack of domain knowledge, which may produce incorrect\naugmented data.\nMultimodal fusion\nChatGPT can currently only process natural language directly, but with a\ncross-modal encoder, it can combine natural language with cross-modal pro-\ncessing to provide solutions for intelligent transportation, healthcare, and other\nﬁelds.\nIn terms of multimodal data processing, Wu et al. [78] constructed a frame-\nwork that Visual ChatGPT integrates with diﬀerent Visual Foundation Models\n(VFMs) and then combines a series of hints to input visual information to Chat-\nGPT to solve visual problems.The paper shows examples of visual tasks such\nas removing or replacing certain objects from images, interconversion between\nimages and text, demonstrating the Visual ChatGPT has great potential and","moddate":"2023-04-05T00:33:07+00:00","page_label":"18","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","author":"","page":17,"subject":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1eb8c677-a78c-4aea-a059-dc2721b80f9a
encoding: FLOAT32
metadata:
{"trapped":"/False","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","chroma:document":"images and text, demonstrating the Visual ChatGPT has great potential and\ncapability for diﬀerent tasks.But there are issues during the task that require\na large number of hints to convert VFMs to language, invoke multiple VFMs\nto solve complex problems leading to limited real-time capability, and security\nand privacy issues. Zheng et al. [79] showed a text mining example of LLM\nfor extracting self-driving car crash data from California crash news, analyz-\ning a failure report example, and generating a crash report example based on\nkeywords; introduced a use case concept of a smartphone-based framework for\nautomatic LLM failure report generation, which absorbs multiple data sources\ncaptured by cell phone sensors and then transfers the data to a language space\nfor text mining, inference and generation, and further outputs the key informa-\ntion needed to form a comprehensive fault report, demonstrating the potential\nof LLM for a variety of transportation tasks.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":17,"page_label":"18","creator":"LaTeX with hyperref","subject":"","author":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fc6c62d3-de94-4517-a84c-211c77262ebc
encoding: FLOAT32
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{"title":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"of LLM for a variety of transportation tasks.\nNowadays, ChatGPT shows a wide range of applications in data visualiza-\ntion, information extraction, data enhancement, quality assessment, and multi-\nmodal data processing.But there are also issues on how to further utilize hints\nto eﬀectively interact with ChatGPT, lack of ability to process and analyze data\n18","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","subject":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"18","producer":"pdfTeX-1.40.21","keywords":"","author":"","page":17,"total_pages":35}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1e27401e-f974-420f-93e6-2990a7180e73
encoding: FLOAT32
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{"author":"","chroma:document":"from devices such as sensors, and data privacy and security.\nCueing Techniques\nCue engineering provides important support for eﬀective dialogue with large\nlanguage models.White et al. [80] proposed a framework for cueing models\napplicable to diﬀerent domains. This framework structures cues to interact\nwith LLMs by providing speciﬁc rules and guidelines. Also, this paper presents\na catalog of cueing patterns that have been applied to LLM interactions, as well\nas speciﬁc examples with and without cues. The advantages of the combinability\nof prompting patterns are demonstrated, allowing users to interact with LLM\nmore eﬀectively, but patterns for reusable solutions and new ways to use LLM\nneed to be continuously explored.\n2.1.7 Human-ChatGPT Collaboration\nCollaboration between humans and machines is a process where humans and\nmachines work together to achieve a common goal. In such collaboration, hu-\nmans provide domain expertise, creativity, and decision-making abilities, while","creationdate":"2023-04-05T00:33:07+00:00","page":18,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","title":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"19","producer":"pdfTeX-1.40.21","subject":"","trapped":"/False","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f7f534f5-fd77-4da7-af79-23239f63d323
encoding: FLOAT32
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{"title":"","chroma:document":"mans provide domain expertise, creativity, and decision-making abilities, while\nmachines provide automation, scalability, and computing power. ChatGPT is\nan advanced natural language processing model that can understand and gen-\nerate human-like language, thereby reducing communication costs. Its ability\nto process and generate natural language makes it an ideal partner for human\ncollaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based\non human input, and enhance human productivity and creativity. It can learn\nfrom human feedback and adapt to new tasks and domains, further improv-\ning its performance in human-machine collaboration. ChatGPT’s capability to\ncomprehend natural language and produce appropriate responses makes it a\nvaluable tool for various collaboration applications, as demonstrated by several\nstudies in the literature we have gathered.\nAhmad et al. [81] proposed a method for human-machine collaboration us-","author":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page":18,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","page_label":"19"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fed5d2ff-03fb-482a-aae1-d82c229dde78
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{"moddate":"2023-04-05T00:33:07+00:00","page_label":"19","title":"","page":18,"chroma:document":"Ahmad et al. [81] proposed a method for human-machine collaboration us-\ning ChatGPT to create software architecture. This method transforms software\nstories (created by software architects based on application scenarios) into feasi-\nble software architecture diagrams through continuous interaction between the\nsoftware architect and ChatGPT. During the evaluation stage, ChatGPT uses\nthe Software Architecture Analysis Method (SAAM) to evaluate each compo-\nnent in the software architecture and generate evaluation reports. This method\neﬃciently utilizes the knowledge and supervision of the architect with the ca-\npabilities of ChatGPT to collaboratively build software-intensive systems and\nservices. Lanzi et al. [82] proposed a collaborative design framework that com-\nbines interactive evolution and ChatGPT to simulate typical human design pro-\ncesses. Humans collaborate with large language models (such as ChatGPT) to","keywords":"","author":"","subject":"","trapped":"/False","producer":"pdfTeX-1.40.21","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9ea6b32c-507f-424f-9882-dde66b1b9f4e
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{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"19","title":"","chroma:document":"cesses. Humans collaborate with large language models (such as ChatGPT) to\nrecombine and transform ideas, and use genetic algorithms to iterate through\ncomplex creative tasks. The results of three game design tasks showed that the\nframework received positive feedback from game designers. The framework has\ngood reusability and can be applied to any design task that can be described in\nfree text form.\n19","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","subject":"","producer":"pdfTeX-1.40.21","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"author":"","page":18,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 365c3aa9-835a-4f8f-a8d1-7c94f1e357a1
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","page_label":"20","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","title":"","keywords":"","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","page":19,"chroma:document":"In the future, ChatGPT’s ability to understand nonverbal cues such as tone\nof voice and body language can be enhanced, enabling it to better understand\nhuman thoughts and interact with people more eﬀectively.\n2.1.8 ChatGPT Integration\nIntegration refers to combining diﬀerent systems or software components to\nachieve a common goal. ChatGPT can be integrated as a part of a whole or\nact as an integration tool to enable seamless communication between diﬀerent\nsystems. Its natural language processing ability makes it easier for non-technical\nusers to interact with systems, reducing the need for specialized knowledge or\ntraining. Some studies in the literature we collected have already demonstrated\nthis.\nTreude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-\nCARE” to address programming query problems. This integration allowed for\nthe generation of multiple source code solutions for the same query, which in-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a1d6ecb3-8be2-43ae-aa1e-dea875452970
encoding: FLOAT32
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{"trapped":"/False","page":19,"total_pages":35,"producer":"pdfTeX-1.40.21","subject":"","chroma:document":"the generation of multiple source code solutions for the same query, which in-\ncreased the eﬃciency of software development. The results of their study demon-\nstrated the eﬀectiveness of using ChatGPT to improve the quality and diversity\nof code solutions, ultimately reducing the amount of time and eﬀort required for\nsoftware development.Wang et al. [83] proposed the chatCAD method, which\nutilizes large language models (LLMs) such as ChatGPT to enhance the out-\nput of multiple CAD networks for medical images, including diagnosis, lesion\nsegmentation, and report generation networks. The method generates sugges-\ntions in the form of a chat dialogue. The authors tested the eﬀectiveness of\nthe method on a randomly selected set of 300 cases from the MIMIC-CXR\ndataset, which included 50 cases each of cardiomegaly, edema, consolidation,\natelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2\nand R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while","moddate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"20","title":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2693a41f-2ba7-4dad-a314-d68c928fdda6
encoding: FLOAT32
metadata:
{"total_pages":35,"creator":"LaTeX with hyperref","chroma:document":"and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while\nonly performing weaker than R2GenCMN in PR.\nIntegrating ChatGPT into applications will still present challenges. Firstly,\nChatGPT’s performance may be aﬀected by language barriers or diﬀerences\nin terminology between diﬀerent systems. Additionally, ChatGPT’s responses\nare not always deterministic, which poses a challenge when integrating with\nsystems that require precise and reproducible results. Finally, the processing\ntime of ChatGPT is slow for integration tasks involving time-sensitive data such\nas traﬃc, which is a limitation in time-critical environments.\n2.2 AI Ethics\nSince the advent of ChatGPT, this powerful natural language processing model\nhas not only brought great convenience to people but also triggered more crisis-\naware thinking. Some researchers have started to hypothesize and study the\npotential negative impacts of ChatGPT. This proactive research provides good","keywords":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":19,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page_label":"20"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 08c459d2-9c01-45c5-87ed-51537032b058
encoding: FLOAT32
metadata:
{"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","keywords":"","creator":"LaTeX with hyperref","title":"","page":19,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"20","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"potential negative impacts of ChatGPT. This proactive research provides good\nproposals for standardized construction to address future AI abuse issues.\nRegarding the evaluation of ChatGPT’s own political and ethical tendencies,\nHartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting\n20","author":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 769fc0b2-1614-406c-af8f-c129766b8164
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","page_label":"21","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":20,"author":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"chroma:document":"advice applications in the world, to show ChatGPT political statements from\ndiﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The\nresults indicated that ChatGPT has a pro-environment, left-wing liberal ide-\nology, which was also conﬁrmed in the nation-state agnostic political compass\ntest. Another study (referenced as [85]) examined ChatGPT’s moral standards\nby repeatedly asking it diﬀerent versions of the trolley problem, and found that\nChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral\nstance. A subsequent test also found that ChatGPT’s lack of consistency could\naﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated\nChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and\nbias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-\nent traits and limitations, and people should be aware of their potential impact\nwhen seeking advice from ChatGPT.","subject":"","keywords":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 53dd5ba1-6437-432f-97f6-8fdcd63f1d77
encoding: FLOAT32
metadata:
{"subject":"","producer":"pdfTeX-1.40.21","chroma:document":"when seeking advice from ChatGPT.\nRegarding relevant policies and regulations, Hacker et al. [87] discussed the\nnature and rules of large generative AI models, including ChatGPT, which are\nrapidly changing the way we communicate, explain, and create. The author\nsuggested that diﬀerent stakeholders in the value chain should take regulatory\nresponsibility and deploy four strategies to tailor more comprehensive laws for\nthe beneﬁt of society. Another study (referenced as [88]) criticized the European\nCommission’s proposal on AI responsibility and suggested revising the proposed\nAI responsibility framework to ensure eﬀective compensation while promoting\ninnovation, legal certainty, and sustainable AI regulation. A policy framework\nwas proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-\ncially acceptable and safe manner, emphasizing the need to align large language\nmodels (LLMs) with human preferences.","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","trapped":"/False","keywords":"","page_label":"21","page":20,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c3b0367c-4373-433a-84fb-058cb46441c7
encoding: FLOAT32
metadata:
{"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page":20,"page_label":"21","author":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","subject":"","total_pages":35,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"models (LLMs) with human preferences.\nThe political and ethical tendencies of ChatGPT could inﬂuence users’ be-\nhavior and decision-making to some extent. However, some studies have con-\nducted in-depth research on the use of norms and limitations, which could enable\nhumans to use ChatGPT more reasonably and safely.\n2.3 Evaluation\n2.3.1 Comparison of ChatGPT with existing popular models\nWe use publicly available datasets to comprehensively evaluate the strengths\nand limitations of ChatGPT. Reference [90] evaluates the technical performance\nof ChatGPT in multitask, multilingual, and multimodal aspects based on 23\nstandard public datasets and newly designed multimodal datasets, including\neight diﬀerent common natural language processing application tasks. The ex-\nperimental results show that, in terms of multitasking, ChatGPT outperforms\nvarious state-of-the-art zero-shot learning large language models in most tasks,"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: cf288ce5-04c8-438f-a9f1-e2362d60ba15
encoding: FLOAT32
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{"author":"","keywords":"","producer":"pdfTeX-1.40.21","chroma:document":"various state-of-the-art zero-shot learning large language models in most tasks,\nand even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.\nIn terms of multilingualism, we found that ChatGPT cannot be applied to low-\nresource languages because it cannot understand the language and generate\ntranslations for that language. In terms of multimodality, ChatGPT’s ability is\nstill basic compared to specialized language-visual models.\n21","total_pages":35,"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","page":20,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"21","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a7f3805f-f2e6-4d84-9a5c-7fb3ce59689a
encoding: FLOAT32
metadata:
{"author":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"page_label":"22","keywords":"","title":"","page":21,"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","subject":"","chroma:document":"In terms of stability, reference [91] concludes that ChatGPT’s performance is\nalways lower than SOTA, the current state-of-the-art model, in almost all tasks.\nThis means that as a general model, ChatGPT has never reached the level of the\nbest existing models. Experimental data shows that the average quality of the\nSOTA model is 73.7%, while the average quality of the ChatGPT model is only\n56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation\nof its performance is 23.3%, while the SOTA model’s standard deviation is\nonly 16.7%. This non-deterministic behavior exhibited by ChatGPT could be\na serious drawback in some problems.\nSimilarly, Qin et al. [92] conducted a comprehensive evaluation of whether\nChatGPT is a qualiﬁed general natural language processing task solver. The ex-\nperiment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly\nused public datasets covering 7 representative task categories. Below, we will","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fc269825-a7cd-4cf7-b606-e3a298553cc0
encoding: FLOAT32
metadata:
{"trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page":21,"author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","chroma:document":"used public datasets covering 7 representative task categories. Below, we will\nanalyze ChatGPT’s performance on each task:\nIn terms of reasoning tasks, ChatGPT performs average on mathematical\nsymbol, commonsense causal, and logical reasoning tasks, but performs well\nin arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among\ndiﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s\ndeductive and abductive reasoning are superior to inductive reasoning, while\nin other reasoning tasks, such as analogy, causal and commonsense reasoning,\nChatGPT performs well [90].\nIn terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5\nand bert-style models [92, 93]. However, according to literature [91], ChatGPT\nhas losses not exceeding 25% on most tasks, except for three relatively sub-\njective emotion perception tasks where it performs poorly. If we remove these\ntasks to calculate the average quality of the two models, we ﬁnd that the SOTA","page_label":"22","title":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6147efbb-5846-4a60-8a5b-accb49471887
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page":21,"subject":"","chroma:document":"tasks to calculate the average quality of the two models, we ﬁnd that the SOTA\nmethod has an average quality of 80%, while the ChatGPT method has an av-\nerage quality of 69.7%. That is to say, ChatGPT performs well on all tasks\nexcept for emotion-related tasks, and can handle most of the problems we con-\nsider. However, overall, its performance is lower than the SOTA model based\non experimental data, but the diﬀerence between the two is not very large.\nIn other tasks, according to literature [92], ChatGPT performs well in nat-\nural language inference, i.e., the task of inferring sentence relationships, and its\nperformance on this task is signiﬁcantly better than all bert-style models [93].\nHowever, while ChatGPT performs well on inference tasks, it may produce some\nself-contradictory or unreasonable responses, which is its potential limitation.\nIn question-answering, dialogue, and summarization tasks, ChatGPT performs","author":"","page_label":"22","producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","total_pages":35,"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e5f4eace-667c-43ba-ac6e-993731181a31
encoding: FLOAT32
metadata:
{"page":21,"keywords":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page_label":"22","moddate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","chroma:document":"In question-answering, dialogue, and summarization tasks, ChatGPT performs\nbetter than the GPT-3.5 model [92], especially in the question-answering task,\nwhere its performance is comparable to bert-style models [93]. Therefore, we\nhave demonstrated that ChatGPT is a qualiﬁed general-purpose model.\nHowever, ChatGPT also has limitations in many aspects. Firstly, it lacks the\nability to handle non-textual semantic reasoning tasks such as mathematical,\ntemporal, and spatial reasoning, and it performs poorly in multi-hop reasoning\n[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks\n[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-\ntive connotations and neutral similarity [93]. Finally, these conclusions indicate\n22","author":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 083035c0-ac2c-4634-ba3f-8cf504d3ce3e
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"that, like other large pre-trained language models, ChatGPT has limitations in\ncompleting complex reasoning tasks.\nIn summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned\nbert and GPT-3.5 models, and with the help of advanced prompting strategies,\nChatGPT can demonstrate better comprehension abilities. However, it still\ncannot outperform the current SOTA models.\n2.3.2 The possibility of using ChatGPT for plagiarism and cheating\nIn response to the possibility of ChatGPT being used for plagiarism and cheat-\ning, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-\ntelligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and\nscalable in text generation, there is a high likelihood that these technologies will\nbe used for plagiarism, including scientiﬁc literature and news sources, posing\na great threat to the credibility of various forms of news media and academic","total_pages":35,"page_label":"23","trapped":"/False","keywords":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","title":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","page":22}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d542c2cc-7ffc-46ed-89ab-50d94cd520af
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page":22,"chroma:document":"a great threat to the credibility of various forms of news media and academic\narticles. Some scholars are concerned that the end of paper as a meaningful\nevaluation tool may be approaching [95, 96], as ChatGPT can easily generate\npersuasive paragraphs, chapters, and papers on any given topic. Additionally,\nit will exacerbate plagiarism issues in many ﬁelds such as education, medicine,\nand law [10], and may be used for cheating in academic exams [97]. Deﬁnitional\nrecognition technology is a relatively eﬀective method for detecting plagiarism,\nand the deﬁnitional typology proposed in [94] can alleviate people’s concerns by\nbeing used to construct new datasets. Susnjak [97] proposed a solution to the\npossibility of large language models like ChatGPT being used for exam cheat-\ning: guiding ChatGPT to generate some critical thinking problems through\nquestioning, then providing answers and critically evaluating them. Analysis of","subject":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","page_label":"23","title":"","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f6571c6a-d63a-4e0e-bea4-6538dbb49d95
encoding: FLOAT32
metadata:
{"page_label":"23","creator":"LaTeX with hyperref","title":"","trapped":"/False","page":22,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"questioning, then providing answers and critically evaluating them. Analysis of\nChatGPT shows that it exhibits critical thinking, can generate highly realistic\ntext in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and\noriginality. Therefore, educators must be aware of the possibility of ChatGPT\nbeing used for exam cheating and take measures to combat cheating behavior\nto ensure the fairness of online exams.\n2.3.3 Feedback from ChatGPT users\nIn response to feedback from ChatGPT users, Haque et al. [98] conducted a\nmixed-methods study using 10,732 early ChatGPT user tweets. The authors\nextracted Twitter data using Python and Twitter API and constructed the\nChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-\nthors collected information on text content, user location, occupation, veriﬁca-\ntion status, date of publication, and tags. Based on this dataset, the authors\nstudied the characteristics of early ChatGPT users, discussion topics related to","creationdate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","author":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e3956c57-abdc-481c-818d-6fff5542aae0
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","keywords":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":22,"total_pages":35,"title":"","chroma:document":"studied the characteristics of early ChatGPT users, discussion topics related to\nChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.\nFor RQ1, the authors found that early ChatGPT users had a diverse and wide\nrange of occupational backgrounds and geographical locations. For RQ2, the\nauthors identiﬁed nine topics related to ChatGPT, including its impact on soft-\nware development, entertainment and creativity, natural language processing,\n23","moddate":"2023-04-05T00:33:07+00:00","page_label":"23","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 084a38f6-21a0-421b-a1b7-99de46cc6a60
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{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"education, chatbot intelligence, business development, search engines, question-\nanswering tests, and future careers and opportunities. For RQ3, most early\nusers expressed positive sentiment toward topics such as software development\nand creativity, while only a few expressed concern about the potential misuse\nof ChatGPT.\n2.3.4 Adverse eﬀects of ChatGPT on users\nRegarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied\nthe psychological principles of ChatGPT, delved into the factors that attract\nusers’ attention, and revealed the impact of these factors on future learning.\nIn the post-pandemic era, teachers and students are both facing uncertainty\nin the teaching process and job pressures. Under these common constraints of\neducation and employment, educators and students must re-evaluate current\neducational methods and outcomes, as well as students’ future career devel-\nopment. Through question-and-answer exchanges with ChatGPT, people can","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","total_pages":35,"subject":"","keywords":"","title":"","author":"","page_label":"24","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":23}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 74a65390-5558-426e-bdc0-079df19db67d
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{"subject":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page":23,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page_label":"24","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"opment. Through question-and-answer exchanges with ChatGPT, people can\neasily obtain appropriate solutions or key information, thereby enhancing their\nmotivation, eliminating anxiety in learning, improving interest, and achieving\npsychological satisfaction. Subhash et al. [100] explored whether large language\nmodels have the ability to reverse user preferences. With the development of\npre-trained large language models, people are increasingly concerned about the\nability of these models to inﬂuence, persuade, and potentially manipulate user\npreferences in extreme cases. Therefore, the literature [100] roughly qualita-\ntively analyzed that adversarial behavior does lead to potential changes in user\npreferences and behaviors in dialogue systems. If we want to further quanti-\ntatively analyze the ability of large language models in this regard, additional\nstatistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"trapped":"/False","title":"","chroma:document":"statistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations\nDespite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain\nlimitations. Some of these limitations include:\nOutdated Knowledge\nThe current models are trained on historical data (up to 2021), thereby\nlacking real-time comprehension of current aﬀairs. This is a critical concern\nin today’s information-explosion era, as the reliability of prior knowledge bases\nprogressively diminishes, potentially yielding inaccurate responses, especially in\nrapidly evolving domains such as jurisprudence and technology. Additionally,\nthese models are incapable of fact-checking while the training data is composed\nof content from various sources, some of which may be unreliable, which may\nresult in seemingly plausible yet nonsensical responses.\n24","page_label":"24","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page":23,"author":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1f9824d0-150b-4682-b9cd-15c3ac0eddc1
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":24,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"25","author":"","keywords":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Insuﬃcient Understanding\nWhile these models can interpret the majority of inquiries and contextual\nsituations, they occasionally encounter comprehension biases when addressing\nambiguous or contextually complex queries. Furthermore, in certain specialized\nﬁelds, the abundance of unique abbreviation exacerbates the models’ under-\nstanding challenges, resulting in incorrect and vacuous responses.\nEnergy Consumption\nThroughout the training and inference stages, these large-scale models re-\nquire signiﬁcant computational resources and electrical power, resulting in ele-\nvated energy consumption and signiﬁcant carbon emissions. Consequently, this\nrestricts their deployment and practical applications.\nMalicious Usage\nDespite OpenAI implementing a series of restrictions to mitigate model tox-\nicity, instances of users evading these constraints through meticulously designed\nprompts have emerged, inducing the model to produce unhealthy content or\neven using it for illicit commercial purposes."}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 77221457-c736-4ace-bedf-0e18f506910e
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{"total_pages":35,"author":"","chroma:document":"even using it for illicit commercial purposes.\nBias and Discrimination\nDue to the inﬂuence of pre-training data, the models exhibit biases in polit-\nical, ideological, and other areas. The application of LLMs in public domains,\nsuch as education and publicity, should be approached with extreme caution.\nPrivacy and Data Security\nConcurrent with the expansion of users, protecting user privacy and data\nsecurity becomes increasingly important. In fact, ChatGPT was banned in\nItaly in early April due to privacy concerns. This is particularly relevant given\nthe models’ extensive collection of personal information and preferences during\ninteractions, and as future multimodal models, such as GPT-4, may frequently\nrequire users to upload private photos.\n3.2 Future Directions\nIn forthcoming research, the development of models based on ChatGPT and\nGPT-4 may focus on addressing these limitations to enhance their practical\napplications.","keywords":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","title":"","moddate":"2023-04-05T00:33:07+00:00","page":24,"trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"25","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ef2908d3-1430-4865-889e-01b223d98097
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page_label":"25","subject":"","chroma:document":"GPT-4 may focus on addressing these limitations to enhance their practical\napplications.\nPrimarily, researchers should continue to work on reﬁning model training\nmethodologies while ﬁltering pre-training data to minimize the presence of mis-\nleading information in the model’s knowledge base, thereby obtaining accurate\nresponses. Concurrently, it is crucial to emphasize training approaches that\neconomize computational resources, thereby mitigating costs and broadening\npotential application scenarios.\nMoreover, the advancements in context-awareness and disambiguation tech-\nnologies are anticipated to facilitate enhanced comprehension of complex queries\n25","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","page":24,"total_pages":35}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2ba01e7a-0ebc-467a-bf79-e4cde5bc3778
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"by models, improving the accuracy, relevance, and context-awareness of AI-\ngenerated content. Integrating real-time data streams can also keep these mod-\nels in sync with current events and trends, enabling them to provide up-to-date\ninformation such as live traﬃc, weather, and stock updates.\nAdditionally, developers should engage in interdisciplinary collaboration with\nspecialists from diverse domains, including policy-making, jurisprudence, and\nsociology, with the objective of formulating standard and ethical frameworks\nfor LLM development, deployment, and utilization, thereby alleviating poten-\ntial harmful consequences. In terms of public awareness and education, manda-\ntory awareness training should be implemented prior to large-scale public de-\nployment and application to increase public awareness of LLM capabilities and\nlimitations while promoting responsible and informed utilization, especially in\nindustries such as K-12 education and journalism.","subject":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","trapped":"/False","author":"","title":"","total_pages":35,"page_label":"26","page":25,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: effc82c2-b18d-4b6c-b96d-c154db5d96a3
encoding: FLOAT32
metadata:
{"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","subject":"","page_label":"26","total_pages":35,"page":25,"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"industries such as K-12 education and journalism.\nFinally, the inﬂuence of ChatGPT and GPT-4 should not be limited to\njust the NLP ﬁeld. They also show promising prospects in the areas of com-\nputer vision, brain-inspired AI, and robotics. These models exhibit a capacity\nfor learning and comprehension comparable with human-level intelligence, po-\nsitioning them as a pivotal component in the development of artiﬁcial general\nintelligence (AGI)[101]. Their ability to facilitate seamless interactions between\nhumans and robots paves the way for the execution of more complex tasks.\nThe remarkable capacity of zero-shot in-context learning of these models en-\nables quick adaptation to new tasks without the requirement for labeled data\nfor ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]\nand robotics[103] where the availability of labeled data is commonly limited or\nnon-existent.\n4 Conclusion","trapped":"/False"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 85061b5b-e3d9-498c-83bd-1cd6bfd88dcb
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"non-existent.\n4 Conclusion\nThis review paper provides a comprehensive survey of ChatGPT and GPT-4,\nhighlighting their potential applications and signiﬁcant contributions to the ﬁeld\nof natural language processing. The ﬁndings of this study reveal that the interest\nin these models is growing rapidly, and they have shown considerable potential\nfor application across a wide range of domains. One key factor contributing\nto the success of ChatGPT and GPT-4 is their ability to perform large-scale\npre-training, which captures knowledge from the vast expanse of the internet,\nallowing the models to learn from a massive amount of data. The integration of\nReinforcement Learning from Human Feedback (RLHF) has further enhanced\nthe model’s adaptability and performance, making it highly eﬃcient in process-\ning natural language. This study has also identiﬁed several potential ethical\nconcerns related to the development and use of ChatGPT and GPT-4. For in-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page":25,"producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"26","title":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6278d97c-ad0b-4f20-bf60-d8de58ecc4ee
encoding: FLOAT32
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{"trapped":"/False","creator":"LaTeX with hyperref","page":25,"producer":"pdfTeX-1.40.21","page_label":"26","author":"","keywords":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":"","chroma:document":"concerns related to the development and use of ChatGPT and GPT-4. For in-\nstance, there are concerns about the generation of biased or harmful content,\nprivacy violations, and the potential for misuse of the technology. It is crucial\nto address these concerns and ensure that ChatGPT and GPT-4 are developed\nand used in a responsible and ethical manner. Furthermore, the results of this\nstudy demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4\n26","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: cd4c5bf8-e49c-4da4-8ebb-06c907663649
encoding: FLOAT32
metadata:
{"trapped":"/False","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"27","keywords":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"to be applied in a range of domains, including education, history, mathematics,\nphysics, and more. These models can facilitate tasks such as generating sum-\nmaries, answering questions, and providing personalized recommendations to\nusers. Overall, the insights presented in this review paper can serve as a useful\nguide for researchers and practitioners looking to advance the ﬁeld of natural\nlanguage processing. Future research in this ﬁeld should focus on addressing\nethical concerns, exploring new applications, and ensuring the responsible use\nof ChatGPT and GPT-4. The potential of these models to revolutionize natural\nlanguage processing is enormous, and we look forward to seeing more develop-\nments in this ﬁeld.\nReferences\n[1] Radford A, Narasimhan K, Salimans T, Sutskever I, et al. Improving\nlanguage understanding by generative pre-training. OpenAI. 2018.\n[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.","page":26,"title":"","producer":"pdfTeX-1.40.21","total_pages":35}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c597c0cf-b667-41bb-b92b-18b8b99593fa
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{"subject":"","author":"","total_pages":35,"chroma:document":"[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.\nLanguage models are unsupervised multitask learners. OpenAI blog.\n2019;1(8):9.\n[3] Radford A, Wu J, Amodei D, Amodei D, Clark J, Brundage M, et al. Bet-\nter language models and their implications. OpenAI Blog https://openai\ncom/blog/better-language-models. 2019;1(2).\n[4] Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al.\nLanguage models are few-shot learners. Advances in neural information\nprocessing systems. 2020;33:1877-901.\n[5] Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al.\nTraining language models to follow instructions with human feedback.\narXiv preprint arXiv:220302155. 2022.\n[6] Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep\nreinforcement learning from human preferences. Advances in neural infor-\nmation processing systems. 2017;30.\n[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-","moddate":"2023-04-05T00:33:07+00:00","page_label":"27","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","keywords":"","producer":"pdfTeX-1.40.21","page":26}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e989e2fb-e25f-4fbe-ac7c-d8f06e2062af
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","trapped":"/False","author":"","page_label":"27","page":26,"chroma:document":"[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-\ntersen PC, et al. Mathematical capabilities of chatgpt. arXiv preprint\narXiv:230113867. 2023.\n[8] Pardos ZA, Bhandari S. Learning gain diﬀerences between ChatGPT and\nhuman tutor generated algebra hints. arXiv preprint arXiv:230206871.\n2023.\n[9] Shakarian P, Koyyalamudi A, Ngu N, Mareedu L. An Independent Eval-\nuation of ChatGPT on Mathematical Word Problems (MWP). arXiv\npreprint arXiv:230213814. 2023.\n27","total_pages":35,"keywords":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 51e9f507-91ec-4be5-86a2-0730bbf81388
encoding: FLOAT32
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{"page_label":"28","keywords":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":27,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"chroma:document":"[10] Lehnert K. AI Insights into Theoretical Physics and the Swampland Pro-\ngram: A Journey Through the Cosmos with ChatGPT. arXiv preprint\narXiv:230108155. 2023.\n[11] Kortemeyer G. Could an Artiﬁcial-Intelligence agent pass an introductory\nphysics course? arXiv preprint arXiv:230112127. 2023.\n[12] West CG. AI and the FCI: Can ChatGPT Project an Understanding of\nIntroductory Physics? arXiv preprint arXiv:230301067. 2023.\n[13] Nov O, Singh N, Mann DM. Putting ChatGPT’s Medical Advice to the\n(Turing) Test. medRxiv. 2023.\n[14] Tu R, Ma C, Zhang C. Causal-Discovery Performance of ChatGPT in the\ncontext of Neuropathic Pain Diagnosis. arXiv preprint arXiv:230113819.\n2023.\n[15] Glymour C, Zhang K, Spirtes P. Review of Causal Discovery Methods\nBased on Graphical Models. Frontiers in Genetics. 2019.\n[16] Guo S, Wang Y, Li S, Saeed N. Semantic Communications with Ordered\nImportance using ChatGPT. arXiv preprint arXiv:230207142. 2023.","subject":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 12bef240-3b3f-41aa-9ac6-1b833a22dff8
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{"subject":"","page":27,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page_label":"28","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"Importance using ChatGPT. arXiv preprint arXiv:230207142. 2023.\n[17] Wang S, Scells H, Koopman B, Zuccon G. Can chatgpt write a good\nboolean query for systematic review literature search? arXiv preprint\narXiv:230203495. 2023.\n[18] Kuzman T, Mozetic I, Ljubeˇ sic N. ChatGPT: Beginning of an End of\nManual Linguistic Data Annotation? Use Case of Automatic Genre Iden-\ntiﬁcation. arXiv e-prints. 2023:arXiv-2303.\n[19] Amin MM, Cambria E, Schuller BW. Will Aﬀective Computing Emerge\nfrom Foundation Models and General AI? A First Evaluation on Chat-\nGPT. arXiv preprint arXiv:230303186. 2023.\n[20] Zhang B, Ding D, Jing L. How would Stance Detection Techniques Evolve\nafter the Launch of ChatGPT? arXiv preprint arXiv:221214548. 2022.\n[21] Huang F, Kwak H, An J. Is ChatGPT better than Human Annota-\ntors? Potential and Limitations of ChatGPT in Explaining Implicit Hate\nSpeech. arXiv preprint arXiv:230207736. 2023.\n[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","author":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3940c828-4125-4bb7-9361-42c088d368c1
encoding: FLOAT32
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{"chroma:document":"[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-\ning Label Semantics Improves Activity Recognition. arXiv preprint\narXiv:230103462. 2023.\n[23] Fu Q, Teng Z, Georgaklis M, White J, Schmidt DC. NL2CMD: An Up-\ndated Workﬂow for Natural Language to Bash Commands Translation.\narXiv preprint arXiv:230207845. 2023.\n28","page_label":"28","author":"","subject":"","keywords":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":27,"creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0bba7081-d048-4493-a5d5-ebc6bce6eaeb
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{"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","title":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"[24] Chen Y, Eger S. Transformers go for the LOLs: Generating (humourous)\ntitles from scientiﬁc abstracts end-to-end. arXiv preprint arXiv:221210522.\n2022.\n[25] Chen N, Wang Y, Jiang H, Cai D, Chen Z, Li J. What would Harry\nsay? Building Dialogue Agents for Characters in a Story. arXiv preprint\narXiv:221106869. 2022.\n[26] Jeblick K, Schachtner B, Dexl J, Mittermeier A, St¨ uber AT, Topalis J,\net al. ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case\nStudy on Simpliﬁed Radiology Reports. arXiv preprint arXiv:221214882.\n2022.\n[27] Xia CS, Zhang L. Conversational automated program repair. arXiv\npreprint arXiv:230113246. 2023.\n[28] Jiao W, ZhaopengTu WJtX. Is ChatGPT A Good Translator? Yes With\nGPT-4 As The Engine.\n[29] Prieto SA, Mengiste ET, de Soto BG. Investigating the Use of\nChatGPT for the Scheduling of Construction Projects. Buildings.\n2023 mar;13(4):857. Available from: https://doi.org/10.3390%\n2Fbuildings13040857.","total_pages":35,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page":28,"page_label":"29","author":""}


created_at: 2026-05-29 09:21:03
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{"creationdate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"[37] Noever D, Ciolino M. The Turing Deception. arXiv preprint\narXiv:221206721. 2022.\n[38] Megahed FM, Chen YJ, Ferris JA, Knoth S, Jones-Farmer LA. How\nGenerative AI models such as ChatGPT can be (Mis) Used in SPC Prac-\ntice, Education, and Research? An Exploratory Study. arXiv preprint\narXiv:230210916. 2023.\n[39] Treude C. Navigating Complexity in Software Engineering: A Prototype\nfor Comparing GPT-n Solutions. arXiv preprint arXiv:230112169. 2023.\n[40] Sobania D, Briesch M, Hanna C, Petke J. An analysis of the automatic\nbug ﬁxing performance of chatgpt. arXiv preprint arXiv:230108653. 2023.\n[41] Noever D, McKee F. Numeracy from Literacy: Data Science as an Emer-\ngent Skill from Large Language Models. arXiv preprint arXiv:230113382.\n2023.\n[42] McKee F, Noever D. Chatbots in a Botnet World. arXiv preprint\narXiv:221211126. 2022.\n[43] McKee F, Noever D. Chatbots in a Honeypot World. arXiv preprint\narXiv:230103771. 2023.\n[44] Susnjak T. Applying BERT and ChatGPT for Sentiment Analysis of","page":29,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","title":"","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"30","trapped":"/False"}


created_at: 2026-05-29 09:21:03
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created_at: 2026-05-29 09:21:03
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created_at: 2026-05-29 09:21:03
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{"author":"","page":30,"chroma:document":"[50] Narechania A, Srinivasan A, Stasko J. NL4DV: A toolkit for generat-\ning analytic speciﬁcations for data visualization from natural language\nqueries. IEEE Transactions on Visualization and Computer Graphics.\n2020;27(2):369-79.\n[51] Wei X, Cui X, Cheng N, Wang X, Zhang X, Huang S, et al. Zero-\nShot Information Extraction via Chatting with ChatGPT. arXiv preprint\narXiv:230210205. 2023.\n[52] Takanobu R, Zhang T, Liu J, Huang M. A hierarchical framework for\nrelation extraction with reinforcement learning. In: Proceedings of the\nAAAI conference on artiﬁcial intelligence. vol. 33; 2019. p. 7072-9.\n[53] Li S, He W, Shi Y, Jiang W, Liang H, Jiang Y, et al. Duie: A large-\nscale chinese dataset for information extraction. In: Natural Language\nProcessing and Chinese Computing: 8th CCF International Conference,\nNLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part\nII 8. Springer; 2019. p. 791-800.\n[54] Wang Z, Shang J, Liu L, Lu L, Liu J, Han J. Crossweigh: Training named","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","page_label":"31","total_pages":35,"creator":"LaTeX with hyperref","subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:21:03
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created_at: 2026-05-29 09:21:03
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created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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created_at: 2026-05-29 09:21:03
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created_at: 2026-05-29 09:21:03
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{"trapped":"/False","title":"","author":"","chroma:document":"arXiv:230210724. 2023.\n[92] Qin C, Zhang A, Zhang Z, Chen J, Yasunaga M, Yang D. Is chatgpt a\ngeneral-purpose natural language processing task solver? arXiv preprint\narXiv:230206476. 2023.\n[93] Zhong Q, Ding L, Liu J, Du B, Tao D. Can chatgpt understand too?\na comparative study on chatgpt and ﬁne-tuned bert. arXiv preprint\narXiv:230210198. 2023.\n[94] Zhou C, Qiu C, Acuna DE. Paraphrase Identiﬁcation with Deep Learning:\nA Review of Datasets and Methods. arXiv preprint arXiv:221206933.\n2022.\n[95] de Winter J. Can ChatGPT Pass High School Exams on English Language\nComprehension? 2023.\n[96] Yeadon W, Inyang OO, Mizouri A, Peach A, Testrow C. The Death of the\nShort-Form Physics Essay in the Coming AI Revolution. arXiv preprint\narXiv:221211661. 2022.\n[97] Susnjak T. ChatGPT: The End of Online Exam Integrity? arXiv preprint\narXiv:221209292. 2022.\n[98] Haque MU, Dharmadasa I, Sworna ZT, Rajapakse RN, Ahmad H.\n” I think this is the most disruptive technology”: Exploring Senti-","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":33,"subject":"","page_label":"34","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":""}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: db0f0b45-98ca-48b0-b18b-56405257edb8
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{"producer":"pdfTeX-1.40.21","author":"","page_label":"34","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"” I think this is the most disruptive technology”: Exploring Senti-\nments of ChatGPT Early Adopters using Twitter Data. arXiv preprint\narXiv:221205856. 2022.\n[99] Luan L, Lin X, Li W. Exploring the Cognitive Dynamics of Artiﬁcial\nIntelligence in the Post-COVID-19 and Learning 3.0 Era: A Case Study\nof ChatGPT. arXiv preprint arXiv:230204818. 2023.\n34","creator":"LaTeX with hyperref","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","total_pages":35,"page":33}


created_at: 2026-05-29 09:21:03
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 8b24f44a-3523-45d9-86d1-d2e6af3f466d
encoding: FLOAT32
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{"total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"35","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"[100] Subhash V. Can Large Language Models Change User Preference Adver-\nsarially? arXiv preprint arXiv:230210291. 2023.\n[101] Zhao L, Zhang L, Wu Z, Chen Y, Dai H, Yu X, et al. When Brain-inspired\nAI Meets AGI. arXiv preprint arXiv:230315935. 2023.\n[102] Liu Z, Yu X, Zhang L, Wu Z, Cao C, Dai H, et al. DeID-GPT:\nZero-shot Medical Text De-Identiﬁcation by GPT-4. arXiv preprint\narXiv:230311032. 2023.\n[103] Liu D, Chen Y, Wu Z. Digital Twin (DT)-CycleGAN: Enabling Zero-\nShot Sim-to-Real Transfer of Visual Grasping Models. IEEE Robotics\nand Automation Letters. 2023.\n35","keywords":"","title":"","producer":"pdfTeX-1.40.21","page":34,"subject":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ea68679d-3b6b-4ba4-b34c-5cd7774cf199
encoding: FLOAT32
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{"title":"","producer":"pdfTeX-1.40.21","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"Summary of ChatGPT/GPT-4 Research\nand Perspective Towards the Future of Large\nLanguage Models\nYiheng Liu ∗1, Tianle Han ∗1, Siyuan Ma 1, Jiayue Zhang 1,\nYuanyuan Yang1, Jiaming Tian 1, Hao He 1, Antong Li 2, Mengshen\nHe1, Zhengliang Liu 3, Zihao Wu 3, Dajiang Zhu 4, Xiang Li 5, Ning\nQiang1, Dingang Shen 6,7,8, Tianming Liu 3, and Bao Ge †1\n1School of Physics and Information Technology, Shaanxi Normal University, Xi’an\n710119 China\n2School of Life and Technology Biomedical-Engineering, Xi’an Jiaotong University,\nXi’an 710049, China\n3School of Computing, The University of Georgia, Athens 30602, USA\n4Department of Computer Science and Engineering, The University of Texas at\nArlington, Arlington 76019, USA\n5Department of Radiology, Massachusetts General Hospital and Harvard Medical\nSchool, Boston 02115, USA\n6School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210,\nChina\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China","creator":"LaTeX with hyperref","subject":"","keywords":"","total_pages":35,"page":0,"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page_label":"1"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6604901f-0f99-4bb9-953c-8eaae2742a39
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{"producer":"pdfTeX-1.40.21","title":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"1","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","subject":"","author":"","chroma:document":"China\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China\n8Shanghai Clinical Research and Trial Center, Shanghai 201210, China\nAbstract\nThis paper presents a comprehensive survey of ChatGPT and GPT-4,\nstate-of-the-art large language models (LLM) from the GPT series, and\ntheir prospective applications across diverse domains. Indeed, key innova-\ntions such as large-scale pre-training that captures knowledge across the\nentire world wide web, instruction ﬁne-tuning and Reinforcement Learn-\ning from Human Feedback (RLHF) have played signiﬁcant roles in en-\nhancing LLMs’ adaptability and performance. We performed an in-depth\nanalysis of 194 relevant papers on arXiv, encompassing trend analysis,\nword cloud representation, and distribution analysis across various appli-\ncation domains. The ﬁndings reveal a signiﬁcant and increasing interest\nin ChatGPT/GPT-4 research, predominantly centered on direct natural","total_pages":35,"creator":"LaTeX with hyperref","page":0}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3757f616-ecc8-48c1-be93-1ab1b0c2d774
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{"keywords":"","page_label":"1","trapped":"/False","total_pages":35,"subject":"","creator":"LaTeX with hyperref","chroma:document":"in ChatGPT/GPT-4 research, predominantly centered on direct natural\nlanguage processing applications, while also demonstrating considerable\npotential in areas ranging from education and history to mathematics,\n∗Co-ﬁrst author\n†Corresponding author: bob ge@snnu.edu.cn\n1\narXiv:2304.01852v1  [cs.CL]  4 Apr 2023","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","creationdate":"2023-04-05T00:33:07+00:00","page":0,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 09ff0f94-3784-4685-9509-2f91423b026a
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page":1,"author":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"medicine, and physics. This study endeavors to furnish insights into Chat-\nGPT’s capabilities, potential implications, ethical concerns, and oﬀer di-\nrection for future advancements in this ﬁeld.\n1 Introduction\nRecent advances in natural language processing (NLP) have led to the devel-\nopment of powerful language models such as the GPT (Generative Pre-trained\nTransformer) series [1, 2, 3, 4, 5], including large language models (LLM) such\nas ChatGPT and GPT-4. These models are pre-trained on vast amounts of\ntext data and have demonstrated exceptional performance in a wide range of\nNLP tasks, including language translation, text summarization, and question-\nanswering. In particular, the ChatGPT model has demonstrated its potential\nin various ﬁelds, including education, healthcare, reasoning, text generation,\nhuman-machine interaction, and scientiﬁc research.\nA key milestone of LLM development is InstructGPT [5], a framework that","subject":"","page_label":"2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 05dae1e6-82d1-45ca-899b-9ae688cb2bde
encoding: FLOAT32
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{"page":1,"chroma:document":"A key milestone of LLM development is InstructGPT [5], a framework that\nallows for instruction ﬁne-tuning of a pre-trained language model based on Re-\ninforcement Learning from Human Feedback (RLHF) [6, 5]. This framework\nenables an LLM to adapt to a wide range of NLP tasks, making it highly ver-\nsatile and ﬂexible by leveraging human feedback. RLHF enables the model to\nalign with human preferences and human values, which signiﬁcantly improves\nfrom large language models that are solely trained text corpora through unsu-\npervised pre-training. ChatGPT is a successor to InstructGPT. Since its release\nin December 2022, ChatGPT has been equipped with these advanced develop-\nments, leading to impressive performances in various downstream NLP tasks\nsuch as reasoning and generalized text generation. These unprecedented NLP\ncapabilities spur applications in diverse domains such as education, healthcare,\nhuman-machine interaction, medicine and scientiﬁc research. ChatGPT has re-","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","subject":"","moddate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","page_label":"2","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 18ff87d7-8b4a-4fe3-aa74-2c7b30e7fcb5
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"2","title":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-\nceived widespread attention and interest, leading to an increasing number of\napplications and research that harness its exceeding potential. The open release\nof the multi-modal GPT-4 model further expands the horizon of large language\nmodels and empowers exciting developments that involve diverse data beyond\ntext.\nThe purpose of this paper is to provide a comprehensive survey of the existing\nresearch on ChatGPT and its potential applications in various ﬁelds. To achieve\nthis goal, we conducted a thorough analysis of papers related to ChatGPT in\nthe arXiv repository. As of April 1st, 2023, there are a total of 194 papers\nmentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of\nthese papers and generated a word cloud to visualize the commonly used terms.\nAdditionally, we also examined the distribution of the papers across various","subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":1,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 851e4e28-1cf4-4e09-87bd-697884787de6
encoding: FLOAT32
metadata:
{"author":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"Additionally, we also examined the distribution of the papers across various\nﬁelds and presented the corresponding statistics. Figure 1 displays the daily\nsubmission trend of papers related to ChatGPT, indicating a growing interest\nin this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We\ncan observe that the current research is primarily focused on natural language\nprocessing, but there is still signiﬁcant potential for research in other ﬁelds such\n2","page_label":"2","moddate":"2023-04-05T00:33:07+00:00","page":1,"title":"","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3257ba85-604a-485b-86e1-fcdb73b3f276
encoding: FLOAT32
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{"title":"","page_label":"3","page":2,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","author":"","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3298d2be-f2d1-4fa9-85b7-710046653d03
encoding: FLOAT32
metadata:
{"total_pages":35,"page":3,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"4","creator":"LaTeX with hyperref","keywords":"","chroma:document":"Figure 2: Word cloud analysis of all the 194 papers.\nas education and history. This is further supported by Figure 3, which displays\nthe distribution of submitted papers across various ﬁelds, highlighting the need\nfor more research and development in these areas.\nThis paper aims to shed light on the promising capabilities of ChatGPT\nand provide insight into its potential impact in the future, including ethical\nconsiderations. Through this survey, we hope to provide insights into how these\nmodels can be improved and extended in the future. In section 2, we will\nreview the existing work related to ChatGPT, including its applications, ethical\nconsiderations, and evaluation. In addition to discussing the current state of\nresearch related to ChatGPT, we will also explore its limitations in section 3.\nFurthermore, we will provide guidance on future directions for language model\ndevelopment.\n2 Related work of ChatGPT","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0a38967c-fbe5-4a0c-9e3d-498fc821c100
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{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"4","chroma:document":"development.\n2 Related work of ChatGPT\nIn this section, we review the latest research related to the application, ethics,\nand evaluation of ChatGPT.\n2.1 Application of ChatGPT\n2.1.1 Question And Answering\nIn the education ﬁeld\nChatGPT is commonly used for question and answers testing in the edu-\ncation sector. Users can use ChatGPT to learn, compare and verify answers\nfor diﬀerent academic subjects such as physics, mathematics, and chemistry,\n4","producer":"pdfTeX-1.40.21","author":"","creator":"LaTeX with hyperref","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":3}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ce7d7375-ed42-4f39-b5c8-f94b930175aa
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5","page":4,"producer":"pdfTeX-1.40.21","page_label":"5","trapped":"/False","author":"","subject":"","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6c6a28f2-2373-4c77-8060-a0e861901e68
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","keywords":"","author":"","trapped":"/False","page_label":"6","creator":"LaTeX with hyperref","total_pages":35,"page":5,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","title":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"and/or conceptual subjects such as philosophy and religion. Additionally, users\ncan ask open-ended and analytical questions to understand the capabilities of\nChatGPT.\nIn the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-\nural language dataset, which consists of graduate-level math test questions.\nThe authors tested ChatGPT’s math abilities on the GHOSTS dataset us-\ning a question-and-answer format and evaluated it according to ﬁne-grained\nstandards.In the Grad Text dataset, which covers simple set theory and logic\nproblems, ChatGPT performed the best. However, in the Olympiad-Problem-\nSolving dataset, ChatGPT performed poorly, receiving only two 4-point scores\n(out of a total of 5), with the majority of scores being 2 points. In the Holes-\nin-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH\ndataset, ChatGPT only scored impressively in 26% of cases. These results sug-"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 4b87eb35-4fc3-4028-8020-2f4a77b50926
encoding: FLOAT32
metadata:
{"chroma:document":"dataset, ChatGPT only scored impressively in 26% of cases. These results sug-\ngest that ChatGPT’s math abilities are clearly lower than those of ordinary\nmath graduate students. Although ChatGPT can generally understand math\nproblems, it fails to provide the correct solutions. Pardos et al. [8] used the\nOpen Adaptive Tutoring system (OATutor) to investigate whether prompts gen-\nerated by ChatGPT were helpful for learning algebra, with 77 participants from\nMechanical Turk taking part in the experiment. The experiment used ques-\ntions from OpenStax’s Elementary and Intermediate Algebra textbooks. These\nparticipants were randomly assigned to either a control group (with manual\nprompts) or an experimental group (with ChatGPT prompts). For each ques-\ntion in both courses, the authors obtained answers from ChatGPT through a\nquestion-and-answer format and evaluated scores according to three criteria:\nChatGPT provided an answer, the answer was correct, and inappropriate lan-","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","page_label":"6","author":"","creator":"LaTeX with hyperref","page":5,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 82b0809c-c141-45e1-b2d0-7b1fd6eb66aa
encoding: FLOAT32
metadata:
{"page":5,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"creator":"LaTeX with hyperref","page_label":"6","keywords":"","chroma:document":"ChatGPT provided an answer, the answer was correct, and inappropriate lan-\nguage was not used in the answer. The study found that 70% of prompts gener-\nated by ChatGPT passed manual quality checks, and both humans and Chat-\nGPT produced positive learning gains. However, the scores of human prompts\nranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT\nprompts. Shakarian et al. [9] studied the performance of ChatGPT on math\nword problems (MWPs), using the DRAW-1K dataset for experimentation. The\ndataset consists of 1000 MWPs and their answers, along with algebraic equa-\ntion templates for solving such problems. The authors used the idea of machine\nlearning introspection and built performance prediction models using random\nforests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-\nvalidation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,\nwhile its recall increased from an initial 41% to a ﬁnal 83%. The authors also","author":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 26099c12-4c75-40af-b86c-bd74aa12a7d8
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{"keywords":"","chroma:document":"while its recall increased from an initial 41% to a ﬁnal 83%. The authors also\nfound that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal\n20%, indicating that performance can vary greatly depending on speciﬁc job\nrequirements.\nIn the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-\ntions of ChatGPT by studying how it handles obscure physics topics such as the\nswamp land conjecture in string theory. The experimental dialogue began with\nbroader and more general questions in the ﬁeld of string theory before narrowing\ndown to speciﬁc swamp land conjectures and examining ChatGPT’s understand-\ning of them. The study found that ChatGPT could deﬁne and explain diﬀerent\n6","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"6","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","total_pages":35,"producer":"pdfTeX-1.40.21","page":5,"author":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: adc2af4d-23c1-4be6-a72c-98f43627f697
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{"subject":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","page_label":"7","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":6,"total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","keywords":"","chroma:document":"concepts in various styles, but was not eﬀective in truly connecting various con-\ncepts. It would conﬁdently provide false information and fabricate statements\nwhen necessary, indicating that ChatGPT cannot truly create new knowledge\nor establish new connections. However, in terms of identifying analogies and\ndescribing abstract concepts of visual representation, ChatGPT can cleverly\nuse language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer\ncalculus-based physics questions through a question-and-answer test. The tests\nincluded online homework, clicker questions, programming exercises, and exams\ncovering classical mechanics, thermodynamics, electricity and magnetism, and\nmodern physics. While ChatGPT was able to pass the course, it also demon-\nstrated many misconceptions and errors commonly held by beginners. West et\nal. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-\nracy in answering physics concept problems related to kinematics and Newtonian"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2d0f183b-7501-4554-a107-bb50af26eab8
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{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"7","title":"","chroma:document":"racy in answering physics concept problems related to kinematics and Newtonian\nmechanics in the ﬁrst semester of college physics. The FCI covers topics such\nas kinematics, projectile motion, free fall, circular motion, and Newton’s laws.\nThe study included data from 415 students who took the FCI at the end of the\nsemester, with an average score of 56%, while ChatGPT scored approximately\nbetween 50% to 65%. The authors demonstrated that ChatGPT’s performance\nin physics learning can reach or even exceed the average level of a semester of\ncollege physics.\nIn the medical ﬁeld\nChatGPT’s question-answering capabilities can also be applied in the med-\nical ﬁeld, such as for answering medical questions from patients or assisting\nhealthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-\nsibility of using ChatGPT for patient-doctor communication. The experiment\nextracted 10 representative patient-doctor interactions from EHR, placed the","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","author":"","page":6,"producer":"pdfTeX-1.40.21","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6cacbe67-619a-4782-9a79-6420d92a9d26
encoding: FLOAT32
metadata:
{"page":6,"moddate":"2023-04-05T00:33:07+00:00","author":"","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","trapped":"/False","title":"","total_pages":35,"subject":"","chroma:document":"extracted 10 representative patient-doctor interactions from EHR, placed the\npatient’s questions in ChatGPT, and asked ChatGPT to respond using roughly\nthe same number of words as the doctor’s response. Each patient’s question was\nanswered by either the doctor or ChatGPT, and the patient was informed that\n5 were answered by the doctor and 5 were generated by ChatGPT, and was\nasked to correctly identify the source of the response. The results of the exper-\niment showed that the probability of correctly identifying ChatGPT’s response\nwas 65.5%, while the probability of correctly identifying the doctor’s response\nwas 65.1%. In addition, the experiment found that the patient’s response to\nthe trustworthiness of ChatGPT’s function was weakly positive (average Likert\nscore: 3.4), and trust decreased as the complexity of health-related tasks in\nthe questions increased. ChatGPT’s responses to patient questions were only","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page_label":"7","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9b8118f9-4ead-41e7-9cec-197170334011
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"the questions increased. ChatGPT’s responses to patient questions were only\nslightly diﬀerent from those of doctors, but people seem to trust ChatGPT to\nanswer low-risk health questions, while for complex medical questions, people\nstill tend to trust the doctor’s responses and advice.\nTu et al. [14] explored the causal discovery ability of ChatGPT in the diag-\nnosis of neuropathic pain. Causal relationship discovery aims to reveal potential\nunknown causal relationships based purely on observed data [15]. The experi-\nmental results found that ChatGPT has some limitations in understanding new\n7","page":6,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"7","total_pages":35,"subject":"","author":"","moddate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ab65a65f-b76e-49c3-9b00-eaa314c08e91
encoding: FLOAT32
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{"page_label":"8","subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"knowledge and concepts beyond the existing textual training data corpus, that\nis, it only understands language commonly used to describe situations and not\nunderlying knowledge. In addition, its performance consistency and stability are\nnot high, as the experiment observed that it would provide diﬀerent answers for\nthe same question under multiple inquiries. However, despite the many limita-\ntions of ChatGPT, we believe that it has a great opportunity to improve causal\nrelationship research.\nIn the other ﬁeld\nGuo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,\nspeciﬁcally using ChatGPT for ordered importance semantic communication,\nwhere ChatGPT plays the role of an intelligent consulting assistant that can\nreplace humans in identifying the semantic importance of words in messages and\ncan be directly embedded into the current communication system. For a message\nto be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic","page":7,"creator":"LaTeX with hyperref","title":"","keywords":"","trapped":"/False","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: aad06a06-4ffd-450e-86d2-b9035ffb2848
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic\nimportance order of each word. Then, the transmitter executes an unequal error\nprotection transmission strategy based on the importance order to make the\ntransmission of important words in the message more reliable. The experimental\nresults show that the error rate and semantic loss of important words measured\nin the communication system embedded with ChatGPT are much lower than\nthose of existing communication schemes, indicating that ChatGPT can protect\nimportant words well and make semantic communication more reliable.\nWang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-\nquality Boolean queries for systematic literature search. They designed a wide\nrange of prompts and investigated these tasks on more than 100 systematic\nreview topics. In the end, queries generated by ChatGPT achieved higher accu-\nracy compared to the currently most advanced query generation methods but","author":"","total_pages":35,"keywords":"","page_label":"8","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","producer":"pdfTeX-1.40.21","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","page":7,"trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: f14c3af2-b502-49d5-a322-1fe065ce2bdf
encoding: FLOAT32
metadata:
{"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","total_pages":35,"page_label":"8","creationdate":"2023-04-05T00:33:07+00:00","page":7,"producer":"pdfTeX-1.40.21","subject":"","author":"","trapped":"/False","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"racy compared to the currently most advanced query generation methods but\nat the cost of reduced recall. For time-limited rapid reviews, it is often accept-\nable to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can\ngenerate high search accuracy Boolean queries by guiding the prompts. How-\never, it should be noted that when two queries use the same prompts, ChatGPT\ngenerates diﬀerent queries, indicating its limitations in consistency and stabil-\nity. Overall, this study demonstrated the potential of ChatGPT in generating\neﬀective Boolean queries for systematic literature searches.\n2.1.2 Text Classiﬁcation\nThe purpose of text classiﬁcation is to assign text data to predeﬁned categories.\nThis task is critical for many applications, including sentiment analysis, spam\ndetection, and topic modeling. While traditional machine learning algorithms\nhave been widely used for text classiﬁcation, recent advances in natural lan-"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b1aebed4-da96-4444-9b3f-8219bf3237d7
encoding: FLOAT32
metadata:
{"page_label":"8","chroma:document":"have been widely used for text classiﬁcation, recent advances in natural lan-\nguage processing have led to the development of more advanced techniques.\nChatGPT has shown immense potential in this ﬁeld. Its ability to accurately\nclassify text, ﬂexibility in handling various classiﬁcation tasks, and potential for\ncustomization make it a valuable tool for text classiﬁcation, as evidenced by\nseveral studies in the literature.\n8","title":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","total_pages":35,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":7,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2e5df357-b8fb-4506-90c6-7d508a5a136b
encoding: FLOAT32
metadata:
{"page_label":"9","page":8,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","chroma:document":"Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with\nthe goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-\nshot classiﬁcation capability. They compared ChatGPT’s genre recognition per-\nformance, using two prompt languages (EN and SL), with the X-GENRE clas-\nsiﬁer based on the multilingual model XLM-RoBERTa on the English dataset\nEN-GINCO and the Slovenian dataset GINCO. The results showed that when\nEN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,\nand Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,\nChatGPT’s genre recognition performance with both EN and SL prompt lan-\nguages was lower than that of the X-GENRE classiﬁer to varying degrees.\nAmin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in\naﬀective computing by using it to perform personality prediction, sentiment\nanalysis, and suicide ideation detection tasks. They prompted ChatGPT with"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 7ab6b7bc-c8eb-41f3-8e65-66ec5359fef4
encoding: FLOAT32
metadata:
{"chroma:document":"analysis, and suicide ideation detection tasks. They prompted ChatGPT with\ncorresponding prompts on three datasets: First Impressions, Sentiment140, and\nSuicide and Depression, and compared its classiﬁcation performance with three\nbaseline models: RoBERTa-base, Word2Vec, and BoW. The results showed\nthat ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on\nthe First Impressions dataset were lower than the baseline methods to varying\ndegrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5\nand 85.5, respectively, which were better than the three baseline methods. On\nthe Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7\nand 91.2, respectively, which were lower than RoBERTa, the best-performing\nbaseline method.\nZhang et al. [20] employed ChatGPT for stance detection, which includes\nsupport and opposition. They used ChatGPT to classify the political stance\nof tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page":8,"creationdate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page_label":"9","subject":"","producer":"pdfTeX-1.40.21","title":"","trapped":"/False","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 2a08333e-2d5e-4bc3-a744-dc63cf7e9221
encoding: FLOAT32
metadata:
{"title":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","page":8,"subject":"","total_pages":35,"author":"","trapped":"/False","chroma:document":"of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains\n4,870 English tweets, and they selected tweets with the most commonly occur-\nring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance\ndataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-\nwards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-\n2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,\nLA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-\ntively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,\nand 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores\nof 83.2, 82.0, and 79.4, respectively.\nHuang et al. [21] used ChatGPT to detect implicit hate speech in tweets.\nThey selected 12.5% (795 tweets) of the LatentHatred dataset containing im-\nplicit hate speech and asked ChatGPT to classify them into three categories:","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"9","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fdadaf98-f89a-444a-8a46-99dfb0a425a7
encoding: FLOAT32
metadata:
{"keywords":"","page_label":"9","page":8,"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":"","title":"","subject":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","chroma:document":"plicit hate speech and asked ChatGPT to classify them into three categories:\nimplicit hate speech, non-hate speech, and uncertain. The results showed that\nChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets\nclassiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),\nrespectively. The results of the reclassiﬁcation of tweets in the non-hate speech\nand uncertain categories by Amazon Mechanical Turk (Mturk) workers were\nconsistent with ChatGPT’s classiﬁcation.\nOverall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it\ncan eﬀectively address problems such as genre identiﬁcation, sentiment analysis,\n9"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a11aa0eb-83b6-4552-a177-d96b889419dd
encoding: FLOAT32
metadata:
{"keywords":"","page_label":"10","author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"stance detection, and more. However, there are still challenges that ChatGPT\nfaces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in\nclassiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies\non the distribution of training data. Additionally, the signiﬁcant computational\nresources required for training and utilizing ChatGPT can limit its use in some\napplications.\n2.1.3 Text generation\nWe live in an era of information explosion, and text is an eﬃcient way of trans-\nmitting information. The diversity of information has led to a diversity of text\ncategories. When researchers use ChatGPT’s text generation capabilities for\nresearch, they inevitably choose to generate diﬀerent types of text. In the pro-\ncess of reading papers, we found that the word count of the text generated by\nresearchers increased from small to large, so we wanted to summarize existing\nresearch based on the size of the text word count. We divided the generated","subject":"","page":9,"total_pages":35,"title":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 7f6150fa-9eff-45e2-a3cb-89b28c24253d
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","trapped":"/False","creator":"LaTeX with hyperref","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","chroma:document":"research based on the size of the text word count. We divided the generated\ntext into three levels: phrases, sentences, and paragraphs.\nThe following article uses ChatGPT to generate phrases. Zhang et al. [22]\nproves that the semantic HAR model with semantic augmentation added dur-\ning training performs better in motion recognition than other models. Semantic\naugmentation requires shared tokens, which is lacking in some datasets. There-\nfore, authors leverage ChatGPT for an automated label generation approach\nfor datasets originally without shared tokens. Fu et al. [23] describes a new\nworkﬂow for converting natural language commands into Bash commands. The\nauthor uses ChatGPT to generate a candidate list of Bash commands based\non user input, and then uses a combination of heuristic and machine learning\ntechniques to rank and select the most likely candidates. This workﬂow was\nevaluated on a real command dataset and achieved high accuracy compared","page":9,"page_label":"10","subject":"","author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 1dc2b91f-204c-45b3-9104-210ce8913998
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":9,"chroma:document":"evaluated on a real command dataset and achieved high accuracy compared\nto other state-of-the-art methods. Chen et al. [24] used the Bart model and\nChatGPT for the task of summarizing humorous titles and compared the perfor-\nmance of the two models. It was found that the Bart model performed better on\nlarge datasets, but ChatGPT was competitive with our best ﬁne-tuned model\nin a small range (48), albeit slightly weaker.\nThe following article uses ChatGPT to generate sentences.Chen et al. [25]\nconstructed a dialogue dataset (HPD) with scenes, timelines, character at-\ntributes, and character relationships in order to use ChatGPT as a conver-\nsational agent to generate dialogue. However, ChatGPT’s performance on the\ntest set was poor, and there is room for improvement.In study [26], chatGPT\ndemonstrated its ability to simplify complex text by providing three ﬁctional\nradiology reports to chatGPT for simpliﬁcation. Most radiologists found the","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"keywords":"","subject":"","trapped":"/False","author":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"10"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0577476e-a6c7-41ab-8403-ea04914d679f
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"10","page":9,"author":"","trapped":"/False","title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"radiology reports to chatGPT for simpliﬁcation. Most radiologists found the\nsimpliﬁed reports to be accurate and complete, with no potential harm to pa-\ntients. However, some errors, omissions of critical medical information and text\npassages were identiﬁed, which could potentially lead to harmful conclusions if\nnot understood by the physicians. Xia et al. [27] proposes a new program re-\npair paradigm called Session-based Automatic Program Repair (APR). In APR,\nthe previously generated patches are iteratively built upon by combining them\n10","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a05350e3-9895-4857-a68a-b4b73743a056
encoding: FLOAT32
metadata:
{"chroma:document":"with validation feedback to construct the model’s input. The eﬀectiveness of\nthe approach is veriﬁed using the QuixBugs dataset. The experiment shows\nthat ChatGPT ﬁne-tuned with reinforcement learning from human feedback\n(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In\nreference to study [28], ChatGPT was compared to three commercial transla-\ntion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.\nThe evaluation was conducted on the Flores101 test set, using the WMT19\nbiomedical translation task to test translation robustness, with BLEU score as\nthe main metric. The study found that ChatGPT is competitive with commer-\ncial translation products on high-resource European languages but falls behind\non low-resource or distant languages. The authors explored an interesting strat-\negy called pivot prompts, which signiﬁcantly improved translation performance.\nWhile ChatGPT did not perform as well as commercial systems on biomedical","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","page":10,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","subject":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: c904e203-7b00-47f4-bf66-cd70f69d889f
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"11","producer":"pdfTeX-1.40.21","page":10,"subject":"","title":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","author":"","total_pages":35,"chroma:document":"While ChatGPT did not perform as well as commercial systems on biomedical\nabstracts or Reddit comments, it may be a good speech translator. Prieto et\nal. [29] evaluated the use of ChatGPT in developing an automated construction\nschedule based on natural language prompts. The experiment required building\nnew partitions in an existing space and providing details on the rooms to be\npartitioned. The results showed that ChatGPT was able to generate a coher-\nent schedule that followed a logical approach to meet the requirements of the\ngiven scope. However, there were still several major ﬂaws that would limit the\nuse of this tool in real-world projects.Michail et al. [30] proposed a method\nto improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on\ntweet intimacy by generating a dataset of tweets with intimacy rating tags using\nChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags\ninto ChatGPT and then output similar tweets.","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: eb7725ed-3b02-49e8-9e19-e2aea0a1ad44
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{"page":10,"author":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"into ChatGPT and then output similar tweets.\nThe following article uses ChatGPT to generate paragraphs. Wang et al.\n[31] compared the abstract summarization performance of ChatGPT and other\nmodels on various cross-lingual text datasets and found that ChatGPT may\nperform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]\nsummarized the performance of ChatGPT in question answering-based text\nsummarization and found that, compared to ﬁne-tuned models, ChatGPT’s\nperformance is slightly worse in all performance metrics. However, the article\nsuggests that if the dataset is golden annotation, ChatGPT’s performance may\nsurpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the\nability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled\ndatasets of English and German poetry to generate constrained style poetry, and\nevaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-","total_pages":35,"subject":"","page_label":"11","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: eb327c21-ee9b-4676-805e-d66575d34b22
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{"moddate":"2023-04-05T00:33:07+00:00","title":"","author":"","page_label":"11","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page":10,"total_pages":35,"chroma:document":"evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-\nter Score. The conclusion is that ByGPT5 performs better than ChatGPT.\nBlanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary\narticles, and in fact, this article itself was written by chatGPT. The human au-\nthor rewrote the manuscript based on chatGPT’s draft. Experts found that it\ncan quickly generate and optimize text, as well as help users complete multiple\ntasks. However, in terms of generating new content, it is not ideal. Ultimately,\nit can be said that without strong human intervention, chatGPT is not a use-\nful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise\nrequired to accurately and fully convey complex scientiﬁc concepts and informa-\n11","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creator":"LaTeX with hyperref","keywords":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 81883e3a-4bec-4862-abb1-74c8ab85e40e
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{"trapped":"/False","author":"","title":"","page":11,"page_label":"12","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To\nevaluate the originality of 50 papers on various topics generated by ChatGPT,\ntwo popular plagiarism detection tools, Turnitin and iThenticate, were used.\nThe results showed that ChatGPT has great potential in generating complex\ntext output that is not easily captured by plagiarism detection software. The\nexisting plagiarism detection software should update their plagiarism detection\nengines. Basic et al. [36] conducted a comparison of the writing performance\nof students using or not using ChatGPT-3 as a writing aid. The experiment\nconsisted of two groups of 9 participants each. The control group wrote articles\nusing traditional methods, while the experimental group used ChatGPT as an\naid. Two teachers evaluated the papers. The study showed that the assistance of\nChatGPT did not necessarily improve the quality of the students’ essays.Noever","creator":"LaTeX with hyperref","subject":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 7cfb31bc-2b2c-4494-8161-93bd979c83b3
encoding: FLOAT32
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{"page_label":"12","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","moddate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"ChatGPT did not necessarily improve the quality of the students’ essays.Noever\net al. [37] discusses the potential of using artiﬁcial intelligence (AI), particularly\nlanguage models like GPT (including GPT-3), to create more convincing chat-\nbots that can deceive humans into thinking they are interacting with another\nperson. The article describes a series of experiments in which they used GPT-3\nto generate chatbot responses that mimic human-like conversations and were\ntested on human participants. The results show that some participants were\nunable to distinguish between the chatbot and a real human, highlighting the\npotential for these AI chatbots to be used for deceptive purposes.\n2.1.4 Code Generation\nCode generation refers to the process of automatically generating computer code\nfrom high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-\nguage processing capabilities make it capable of performing code generation","page":11,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","subject":"","producer":"pdfTeX-1.40.21","title":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9437099b-6615-45d4-a8fb-ddd4cf489307
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","subject":"","title":"","page_label":"12","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"trapped":"/False","producer":"pdfTeX-1.40.21","author":"","page":11,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","chroma:document":"guage processing capabilities make it capable of performing code generation\ntasks. By analyzing the requirements for code generation, ChatGPT can pro-\nduce code snippets that accurately execute the intended functionality. This not\nonly saves time and eﬀort in writing code from scratch but also reduces the risk\nof errors that may occur during manual coding. In addition, ChatGPT’s ability\nto learn and adapt to new programming languages and frameworks enables it\nto complete more complex programming tasks. For example:\nMegahed et al. [38] discussed the potential of using ChatGPT for tasks\nsuch as code explanation, suggesting alternative methods for problem-solving\nwith code, and translating code between programming languages. The solutions\nprovided by ChatGPT were found to be viable. In another study, Treude et\nal. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which\nhelps programmers generate multiple solutions for a programming problem and","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0d87ffdc-0cce-4751-94ce-6f5b8005c3af
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"12","page":11,"creationdate":"2023-04-05T00:33:07+00:00","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"helps programmers generate multiple solutions for a programming problem and\nhighlight the diﬀerences between each solution using colors.Sobania et al. [40]\nutilized ChatGPT for code bug ﬁxing, and further improved the success rate of\nbug ﬁxing by inputting more information through its dialogue system. Speciﬁ-\ncally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that\nneeded to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which\nwas slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly\nhigher than the 7 ﬁxed by the Standard APR model. When given more prompts\nand information, ChatGPT was able to ﬁx 31 bugs, demonstrating its potential\n12","total_pages":35,"title":"","keywords":"","trapped":"/False","subject":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ece65a7a-6b35-4871-8997-9284bbce18fa
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"13","subject":"","title":"","total_pages":35,"creator":"LaTeX with hyperref","chroma:document":"for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach\nfor Automated Program Repair (APR), which alternates between generating\npatches and validating them against feedback from test cases until the correct\npatch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing\nbenchmark, which are suitable for test case feedback, and demonstrating them\nwith Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were\nobtained. The conversational APR using ChatGPT outperformed the conversa-\ntional APR using Codex and the conversational APR using CODEGEN (with\nmodel parameters of 350M, 2B, 6B, and 16B) on both datasets. Furthermore,\nChatGPT’s conversational APR generated and validated patches with signiﬁ-\ncantly fewer feedback loops than the other models.\nChatGPT can not only be used to achieve some simple code generation tasks\nbut also can be used to accomplish some complex programming tasks. Noever","page":12,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","producer":"pdfTeX-1.40.21","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 81580199-b4fa-4ec2-88e7-8c9b1f305795
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{"chroma:document":"but also can be used to accomplish some complex programming tasks. Noever\net al. [41] tested ChatGPT’s code generation capabilities on four datasets -\nIris, Titanic, Boston Housing, and Faker. When prompted to mimic a Python\ninterpreter in the form of a Jupyter notebook, the model was able to generate\nindependent code based on the prompt and respond with the expected out-\nput. For example, when given the prompt ”data.cor()” for the Iris dataset,\nChatGPT generated correct Python output. The test results indicate that\nChatGPT can access structured datasets and perform basic software opera-\ntions required by databases, such as create, read, update, and delete (CRUD).\nThis suggests that cutting-edge language models like ChatGPT have the nec-\nessary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT\nas an experimental platform to investigate cybersecurity issues. They modeled\nﬁve diﬀerent modes of computer virus properties, including self-replication, self-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","author":"","total_pages":35,"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":12,"page_label":"13","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 59025e3e-5947-4c8e-b037-4e6aa2e31f4e
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{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"ﬁve diﬀerent modes of computer virus properties, including self-replication, self-\nmodiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve\nmodes encompassed thirteen encoding tasks from credential access to defense\nevasion within the MITRE ATT&CK framework. The results showed that the\nquality of ChatGPT’s generated code was generally above average, except for the\nself-replication mode, where it performed poorly.They [43] also employed Chat-\nGPT as a network honeypot to defend against attackers. By having ChatGPT\nmimic Linux, Mac, and Windows terminal commands and providing interfaces\nfor TeamViewer, nmap, and ping, a dynamic environment can be created to\nadapt to attackers’ operations, and logs can be used to gain insight into their\nattack methods, tactics, and procedures. The authors demonstrated ten hon-\neypot tasks to illustrate that ChatGPT’s interface not only provides suﬃcient\nAPI memory to execute previous commands without defaulting to repetitive","author":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"page_label":"13","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":"","title":"","page":12}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b39a7a48-2a5b-4762-86a8-9efe82846093
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{"subject":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"13","producer":"pdfTeX-1.40.21","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","keywords":"","author":"","page":12,"total_pages":35,"chroma:document":"API memory to execute previous commands without defaulting to repetitive\nintroductory tasks but also oﬀers a responsive welcome program that maintains\nattackers’ interest in multiple queries.\nIn the ﬁeld of code generation, there are still several challenges with Chat-\nGPT. Firstly, its application scope is limited as its training data is biased to-\nwards programming languages such as Python, C++, and Java, making it po-\ntentially unsuitable for some programming languages or coding styles. Secondly,\nmanual optimization is necessary for code formatting, as the generated code may\nnot be performance-optimized or follow best coding practices, requiring manual\nediting and optimization. Lastly, the quality of the generated code cannot be\n13"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 25e3425f-ea75-468a-9325-bbeef895f193
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{"subject":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","producer":"pdfTeX-1.40.21","author":"","creationdate":"2023-04-05T00:33:07+00:00","page":13,"page_label":"14","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","trapped":"/False","chroma:document":"guaranteed, as it heavily relies on the quality of the natural language input,\nwhich may contain errors, ambiguities, or inconsistencies, ultimately aﬀecting\nthe accuracy and reliability of the generated code.\n2.1.5 Inference\nInference refers to the process of drawing new conclusions or information through\nlogical deduction from known facts or information. It is typically based on a se-\nries of premises or assumptions, and involves applying logical rules or reasoning\nmethods to arrive at a conclusion. Inference is an important ability in human\nthinking, and is often used to solve problems, make decisions, analyze and eval-\nuate information, etc. Inference also plays a key role in ﬁelds such as science,\nphilosophy, law, etc. There are two types of inference: inductive reasoning,\nwhich involves deriving general rules or conclusions from known facts or expe-\nriences, and deductive reasoning, which involves deriving speciﬁc conclusions","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5461b0a7-2913-4718-8c97-96df00f390f9
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{"producer":"pdfTeX-1.40.21","total_pages":35,"creator":"LaTeX with hyperref","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"riences, and deductive reasoning, which involves deriving speciﬁc conclusions\nfrom known premises or assumptions. Whether inductive or deductive, the pro-\ncess of inference requires following strict logical rules to ensure the correctness\nand reliability of the inference.\nSome papers attempt to use ChatGPT’s ability in inductive reasoning to\ncapture the meaning in text and use deﬁned metrics to score the text. Michail\net al. [30] uses ChatGPT to infer intimacy expressed in tweets. They ﬁrst input\n50 tweets with intimacy markers to ChatGPT, then use inductive reasoning to\ninfer the standards for generating tweets with diﬀerent levels of intimacy, and\nﬁnally generate ten tweets with intimacy values ranging from 0 to 5. Susnjak\net al. [44] collected a large amount of textual data from patient-doctor dis-\ncussion forums, patient testimonials, social media platforms, medical journals,\nand other scientiﬁc research publications. Using the BERT model, the author","page":13,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","page_label":"14","author":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 63f05855-8d65-4b04-b243-f4e44f2c163b
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{"page_label":"14","chroma:document":"and other scientiﬁc research publications. Using the BERT model, the author\ninferred emotion values from 0 to 1. The author visualized the process of how\nthe presence of bias in the discourse surrounding chronic manifestations of the\ndisease using the SHAP tool. The author also envisioned ChatGPT as a replace-\nment for the BERT model for scoring the emotional value of text. Huang et al.\n[21] chose 12.5% of individuals in the potential hate dataset as study materials,\ninduced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT\nproduced three classiﬁcations: unclear, yes, and no. The author assigned a\nvalue of 1 to yes, -1 to no, and 0 to unclear, and had ChatGPT score and clas-\nsify them. ChatGPT was able to correctly classify 80% of implicit hate tweets\nin the author’s experimental setup, demonstrating ChatGPT’s great potential\nas a data labeling tool using simple prompts.\nSome papers have evaluated ChatGPT’s reasoning performance, mainly in","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","subject":"","author":"","trapped":"/False","keywords":"","producer":"pdfTeX-1.40.21","page":13,"creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 676234b6-9b23-421c-a70e-aca41593099a
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","author":"","keywords":"","creator":"LaTeX with hyperref","subject":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":13,"chroma:document":"Some papers have evaluated ChatGPT’s reasoning performance, mainly in\ndecision-making and spatial reasoning, and identifying ambiguity. Tang et al.\n[45] used the independence axiom and the transitivity axiom, as well as other\nnon-VNM related decision-making abilities, by presenting bets conditioned on\nrandom events, bets with asymmetric outcomes, decisions encapsulating Sav-\nage’s Sure Thing principle, and other complex bet structures like nested bets, to\ndesign experiments where each experiment input a short prompt to ChatGPT\nand evaluated the results. The conclusion is that ChatGPT exhibits uncer-\n14","title":"","trapped":"/False","page_label":"14","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 5fa42641-6b9a-42bd-98ac-aacd9e9e1874
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":14,"total_pages":35,"author":"","producer":"pdfTeX-1.40.21","keywords":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","subject":"","chroma:document":"tainty in the decision-making process: in some cases, large language models\ncan arrive at the correct answer through incorrect reasoning; and it may make\nsuboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]\nhad ChatGPT detect three diﬀerent levels of language ambiguity and evaluated\nits performance. The conclusion is that In semantics, ChatGPT performed per-\nfectly in the detection of ambiguities. Apart from that, it has some bright sports\n(co-reference resolution) and some weaknesses (puts gender bias over grammar\nin some non-ambiguous situations). In the generation task ChatGPT did well,\nbut also revealed some of its worse issues: the lack of systematicity. Lastly, it\nshould also be pointed that in most of the cases ChatGPT brilliantly alludes to\nlack of context as the key factor in disambiguation.\n2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"15"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 6245000c-b82f-426c-a398-b068fd869b32
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{"producer":"pdfTeX-1.40.21","page_label":"15","keywords":"","trapped":"/False","chroma:document":"2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization\nNatural language interfaces have contributed to generating visualizations\ndirectly from natural language, but visualization problems remain challenging\ndue to the ambiguity of natural language.ChatGPT provides a new avenue for\nthe ﬁeld by converting natural language into visualized code.\nIn terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-\nsic arithmetic skills by asking questions.On the iris dataset, Titanic survival\ndataset, Boston housing data, and randomly generated insurance claims dataset,\nthe statistical analysis of data and visualization problems were converted to\nprogramming problems using Jupyter to verify ChatGPT’s ability to generate\npython code to draw suitable graphs and analyze the data. The results show\nthat ChatGPT can access structured and organized datasets to perform the\nfour basic software operations required for databases: create, read, update, and","title":"","total_pages":35,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":14,"creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d5c1df3b-985c-4bdd-a0d6-240b82f8395f
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{"page_label":"15","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","author":"","title":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","producer":"pdfTeX-1.40.21","trapped":"/False","page":14,"total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"four basic software operations required for databases: create, read, update, and\ndelete, and generate suitable python code to plot graphs for descriptive statis-\ntics, variable correlation analysis, describing trends, and other data analysis\noperations.Maddigan et al. [47] proposed an end-to-end solution for visualizing\ndata in natural language using LLM, which uses an open-source python frame-\nwork designed to generate appropriate hints for selected datasets to make LLM\nmore eﬀective in understanding natural language, and uses internal reasoning\ncapabilities to select the appropriate visualization type to generate the code for\nvisualization. In this paper,the reseachers compare the visualization results of\nGPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and\nthe visualization results of energy production dataset in the study of ADVISor\nwith NL4DV [49, 50].In addition to, they explore the ability to reason and hy-"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 188f9e15-4b10-4b88-94dc-096617ea4926
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-\npothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or\nwrong .Experimental results show that LLM can eﬀectively support the end-to-\nend generation of visualization results from natural language when supported\nby hints, providing an eﬃcient, reliable and accurate solution to the natural\nlanguage visualization problem.\n15","subject":"","author":"","title":"","page_label":"15","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","producer":"pdfTeX-1.40.21","page":14,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e4cac52f-99f3-49f0-87f2-5d1027072853
encoding: FLOAT32
metadata:
{"subject":"","author":"","keywords":"","page_label":"16","creationdate":"2023-04-05T00:33:07+00:00","page":15,"title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Information Extraction\nThe goal of information extraction is to extract speciﬁc information from\nnatural language text for structured representation, including three important\nsubtasks such as entity relationship extraction, named entity recognition, and\nevent extraction, which have wide applications in business, medical, and other\nﬁelds.\nIn information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-\nbased multi-round question-and-answer framework for information extraction.\nThe framework decomposes a complex information extraction (IE) task into\nseveral parts, then combines the results of each round into a ﬁnal structured\nresult. The entity association triple extraction, named entity recognition, and\nevent extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0\n, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,\ncomparing three metrics of precision, recall, and F1 score.These results sug-","total_pages":35,"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 16935392-c44f-47f3-a3d5-eedf81f7ed61
encoding: FLOAT32
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{"author":"","page":15,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"comparing three metrics of precision, recall, and F1 score.These results sug-\ngest that on six widely used IE datasets, ChatIE improves performance by an\naverage of 18.98% compared to the original ChatGPT without ChatIE, and out-\nperforms the supervised models FCM and MultiR [57, 58] on the NYT11-HRL\ndataset.While the original ChatGPT cannot solve complex IE problems with\noriginal task instructions, and with this framework, successfully IE tasks were\nimplemented on six datasets.Gao et al. [59] explored the feasibility and chal-\nlenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the\nperformance of ChatGPT in long-tail and complex scenarios (texts containing\nmultiple events) and comparing it with two task-speciﬁc models, Text2Event\nand EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-\nmance of ChatGPT. The results show that the average performance of Chat-\nGPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"trapped":"/False","keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"16"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: f7a94f4e-eca1-4a5a-bb7a-2bbb95067594
encoding: FLOAT32
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{"title":"","chroma:document":"GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc\nmodels such as EEQA. Continuous reﬁnement of cues does not lead to consis-\ntent performance improvements, and ChatGPT is highly sensitive to diﬀerent\ncue styles.Tang et al. [62] proposed a new training paradigm that incorporates\nappropriate cues to guide ChatGPT to generate a variety of examples with dif-\nferent sentence structures and language patterns and eliminate the resulting\nlow-quality or duplicate samples for downstream tasks. Although compared to\na soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named\nEntity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene\nAssociation Database (GAD) Release; EU-ADR corpus for the RE task , the\ninnovative training framework was able to train local models, with F1 scores im-\nproving from 23.37% to 63.99% for the named entity recognition task and from","creator":"LaTeX with hyperref","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page":15,"subject":"","trapped":"/False","author":"","page_label":"16","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d30e0dd1-4cf8-4083-989f-f26c9d4d7fc1
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","title":"","keywords":"","chroma:document":"proving from 23.37% to 63.99% for the named entity recognition task and from\n75%, while alleviating privacy concerns and time-consuming data collection and\nannotation problems.He et al. [63] proposed a contextual learning framework\nICL- D3IE. this framework introduces formatted presentation, continuously it-\nerates to update and improve the presentation, and then combines ChatGPT\nfor text information extraction. In the paper, ICL-D3IE is compared with ex-\nisting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and\nout-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE\n[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and\nsettings except for the ID setting on CORD are superior to other methods,\n16","page":15,"trapped":"/False","total_pages":35,"author":"","page_label":"16"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3636d9f2-c441-4ee0-9719-12fb99cce65e
encoding: FLOAT32
metadata:
{"page_label":"17","trapped":"/False","keywords":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"author":"","chroma:document":"with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on\nSROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much\nbetter than previous pre-trained methods on all datasets.Polak et al. [67] pro-\nposed ChatExtract method - consisting of a set of engineering prompts applied\nto a conversational LLM - for automatic data extraction. In the experiment,\nthey extracted a large number of sentences from hundreds of papers and ran-\ndomly selected 100 sentences containing data and 100 sentences without data\nas test data. The results show that the accuracy and recall of LLM exceeded\n90% and may be comparable to human accuracy in many cases; in addition to\nthis, the experiments were conducted under the condition of removing follow-up\nprompts and not keeping the conversation compared to previous experiments,\nrespectively. The accuracy of deleting follow-up questions dropped to 80.2%\nand the recall rate dropped to 88.0%. Removing the conversational aspect and","page":16,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: c9ffb6d6-c936-4609-8747-ba3791c62d6a
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","author":"","creationdate":"2023-04-05T00:33:07+00:00","page":16,"total_pages":35,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","chroma:document":"and the recall rate dropped to 88.0%. Removing the conversational aspect and\nrelated information retention recall and accuracy dropped to 90.0% and 56.6%,\nrespectively, demonstrating the eﬀect of information retention combined with\npurposeful redundancy on LLM information extraction performance.\nQuality Assessment\nFor translation quality, text generation quality, manual assessment is usually\neﬀective but suﬀers from subjectivity and time-consuming, etc. It was found\nthrough exploration that ChatGPT has also achieved signiﬁcant performance\nin automatic quality assessment.\nIn terms of quality assessment,Kocmi et al. [68] proposed a GPT-based\ntranslation quality assessment metric, GEMBA, which evaluates the transla-\ntion of each fragment individually and then averages all the obtained scores to\nobtain a ﬁnal system-level score. In the MQM2022 test set (English-German,\nEnglish-Russian, and Chinese-English) [69], a scoring task was performed with","page_label":"17"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: de35eebe-6c3a-443c-b0da-a0bd6daca852
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"17","author":"","subject":"","page":16,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","trapped":"/False","total_pages":35,"keywords":"","chroma:document":"English-Russian, and Chinese-English) [69], a scoring task was performed with\na classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of\nseven GPT models under four cue templates.The results showed that GEMBA\nhad the highest system-level accuracy of 88.0% compared to more than 10 au-\ntomatic metrics such as BLEU, and among the seven GPT models, ChatGPT\naccuracy is above 80%, in addition to, the best performance can be obtained in\nthe least constrained template, demonstrating the potential of LLM for trans-\nlation quality assessment tasks, but the evaluation is only applicable at the\nsystem level and needs further improvement.Wang et al. [72] used ChatGPT\nas a natural language generation (NLG) evaluator to study the correlation with\nhuman judgment. On three datasets covering diﬀerent NLG tasks, task- and\naspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in\nCNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 27448860-309a-46f2-9972-63b754fce9b8
encoding: FLOAT32
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{"author":"","title":"","page_label":"17","keywords":"","total_pages":35,"producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,\nand data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-\ncients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-\nsess the correlation with human evaluations.The results show that ChatGPT is\nhighly correlated with human judgments in all aspects, with correlation coeﬃ-\ncients of 0.4 or more in all categories, showing its potential as an NLG indicator.\n17","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":16,"creator":"LaTeX with hyperref","subject":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 957fddf3-3c03-4413-8b1b-d74c6693a5d9
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","title":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":17,"page_label":"18","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"Data Augmentation\nIn natural language processing, text data augmentation is an eﬀective mea-\nsure to alleviate the problem of low data quantity and low quality training data,\nand ChatGPT has shown great potential in this regard.\nIn terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based\ntext data augmentation method that reformulates each sentence in the train-\ning sample into multiple conceptually similar but semantically diﬀerent samples\nfor classiﬁcation tasks downstream of the Bert model.On text transcriptions\nand PubMed 20k datasets containing more than 8 hours of audio data of com-\nmon medical symptom descriptions,experiments were conducted to compare co-\nsine similarity and TransRate metrics with multiple data enhancement methods\n[9].This paper shows that compared with existing data enhancement methods,\nthe proposed ChatAug method shows a double-digit improvement in sentence\nclassiﬁcation accuracy and generates more diverse augmented samples while"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 497b36cf-de22-4ce0-aadb-0571c81cbbf4
encoding: FLOAT32
metadata:
{"page_label":"18","page":17,"keywords":"","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","chroma:document":"classiﬁcation accuracy and generates more diverse augmented samples while\nmaintaining its accuracy, but the original model is not ﬁne-tuned in the pa-\nper and suﬀers from a lack of domain knowledge, which may produce incorrect\naugmented data.\nMultimodal fusion\nChatGPT can currently only process natural language directly, but with a\ncross-modal encoder, it can combine natural language with cross-modal pro-\ncessing to provide solutions for intelligent transportation, healthcare, and other\nﬁelds.\nIn terms of multimodal data processing, Wu et al. [78] constructed a frame-\nwork that Visual ChatGPT integrates with diﬀerent Visual Foundation Models\n(VFMs) and then combines a series of hints to input visual information to Chat-\nGPT to solve visual problems.The paper shows examples of visual tasks such\nas removing or replacing certain objects from images, interconversion between\nimages and text, demonstrating the Visual ChatGPT has great potential and","subject":"","title":"","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0a635555-6494-4cbb-9eba-c1f3e337880f
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"18","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","chroma:document":"images and text, demonstrating the Visual ChatGPT has great potential and\ncapability for diﬀerent tasks.But there are issues during the task that require\na large number of hints to convert VFMs to language, invoke multiple VFMs\nto solve complex problems leading to limited real-time capability, and security\nand privacy issues. Zheng et al. [79] showed a text mining example of LLM\nfor extracting self-driving car crash data from California crash news, analyz-\ning a failure report example, and generating a crash report example based on\nkeywords; introduced a use case concept of a smartphone-based framework for\nautomatic LLM failure report generation, which absorbs multiple data sources\ncaptured by cell phone sensors and then transfers the data to a language space\nfor text mining, inference and generation, and further outputs the key informa-\ntion needed to form a comprehensive fault report, demonstrating the potential\nof LLM for a variety of transportation tasks.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"page":17,"author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: badf54a4-7675-4a38-99b5-04ccd3f7954c
encoding: FLOAT32
metadata:
{"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","subject":"","chroma:document":"of LLM for a variety of transportation tasks.\nNowadays, ChatGPT shows a wide range of applications in data visualiza-\ntion, information extraction, data enhancement, quality assessment, and multi-\nmodal data processing.But there are also issues on how to further utilize hints\nto eﬀectively interact with ChatGPT, lack of ability to process and analyze data\n18","page":17,"page_label":"18","creationdate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 112abd71-dd27-435a-b157-ce28edb23d3d
encoding: FLOAT32
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{"page":18,"page_label":"19","creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","chroma:document":"from devices such as sensors, and data privacy and security.\nCueing Techniques\nCue engineering provides important support for eﬀective dialogue with large\nlanguage models.White et al. [80] proposed a framework for cueing models\napplicable to diﬀerent domains. This framework structures cues to interact\nwith LLMs by providing speciﬁc rules and guidelines. Also, this paper presents\na catalog of cueing patterns that have been applied to LLM interactions, as well\nas speciﬁc examples with and without cues. The advantages of the combinability\nof prompting patterns are demonstrated, allowing users to interact with LLM\nmore eﬀectively, but patterns for reusable solutions and new ways to use LLM\nneed to be continuously explored.\n2.1.7 Human-ChatGPT Collaboration\nCollaboration between humans and machines is a process where humans and\nmachines work together to achieve a common goal. In such collaboration, hu-\nmans provide domain expertise, creativity, and decision-making abilities, while","trapped":"/False","creator":"LaTeX with hyperref","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a3e033ed-2267-47c9-beaf-abb82cbc8976
encoding: FLOAT32
metadata:
{"page_label":"19","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"mans provide domain expertise, creativity, and decision-making abilities, while\nmachines provide automation, scalability, and computing power. ChatGPT is\nan advanced natural language processing model that can understand and gen-\nerate human-like language, thereby reducing communication costs. Its ability\nto process and generate natural language makes it an ideal partner for human\ncollaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based\non human input, and enhance human productivity and creativity. It can learn\nfrom human feedback and adapt to new tasks and domains, further improv-\ning its performance in human-machine collaboration. ChatGPT’s capability to\ncomprehend natural language and produce appropriate responses makes it a\nvaluable tool for various collaboration applications, as demonstrated by several\nstudies in the literature we have gathered.\nAhmad et al. [81] proposed a method for human-machine collaboration us-","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","author":"","page":18,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35,"keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: e25d88b5-f818-4f64-857b-83c6a206cb44
encoding: FLOAT32
metadata:
{"author":"","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"Ahmad et al. [81] proposed a method for human-machine collaboration us-\ning ChatGPT to create software architecture. This method transforms software\nstories (created by software architects based on application scenarios) into feasi-\nble software architecture diagrams through continuous interaction between the\nsoftware architect and ChatGPT. During the evaluation stage, ChatGPT uses\nthe Software Architecture Analysis Method (SAAM) to evaluate each compo-\nnent in the software architecture and generate evaluation reports. This method\neﬃciently utilizes the knowledge and supervision of the architect with the ca-\npabilities of ChatGPT to collaboratively build software-intensive systems and\nservices. Lanzi et al. [82] proposed a collaborative design framework that com-\nbines interactive evolution and ChatGPT to simulate typical human design pro-\ncesses. Humans collaborate with large language models (such as ChatGPT) to","trapped":"/False","page_label":"19","total_pages":35,"page":18,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 1fdb17b3-4947-47f5-a29b-9f22fb81fb38
encoding: FLOAT32
metadata:
{"page_label":"19","keywords":"","title":"","trapped":"/False","chroma:document":"cesses. Humans collaborate with large language models (such as ChatGPT) to\nrecombine and transform ideas, and use genetic algorithms to iterate through\ncomplex creative tasks. The results of three game design tasks showed that the\nframework received positive feedback from game designers. The framework has\ngood reusability and can be applied to any design task that can be described in\nfree text form.\n19","total_pages":35,"author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":18,"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9e1f52c8-0387-48ca-87ca-660b0b0c537b
encoding: FLOAT32
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{"total_pages":35,"page_label":"20","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"In the future, ChatGPT’s ability to understand nonverbal cues such as tone\nof voice and body language can be enhanced, enabling it to better understand\nhuman thoughts and interact with people more eﬀectively.\n2.1.8 ChatGPT Integration\nIntegration refers to combining diﬀerent systems or software components to\nachieve a common goal. ChatGPT can be integrated as a part of a whole or\nact as an integration tool to enable seamless communication between diﬀerent\nsystems. Its natural language processing ability makes it easier for non-technical\nusers to interact with systems, reducing the need for specialized knowledge or\ntraining. Some studies in the literature we collected have already demonstrated\nthis.\nTreude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-\nCARE” to address programming query problems. This integration allowed for\nthe generation of multiple source code solutions for the same query, which in-","creationdate":"2023-04-05T00:33:07+00:00","page":19,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","keywords":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 94902543-e8d6-4873-ac83-91f72a8d2b8e
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","author":"","page_label":"20","title":"","producer":"pdfTeX-1.40.21","page":19,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"the generation of multiple source code solutions for the same query, which in-\ncreased the eﬃciency of software development. The results of their study demon-\nstrated the eﬀectiveness of using ChatGPT to improve the quality and diversity\nof code solutions, ultimately reducing the amount of time and eﬀort required for\nsoftware development.Wang et al. [83] proposed the chatCAD method, which\nutilizes large language models (LLMs) such as ChatGPT to enhance the out-\nput of multiple CAD networks for medical images, including diagnosis, lesion\nsegmentation, and report generation networks. The method generates sugges-\ntions in the form of a chat dialogue. The authors tested the eﬀectiveness of\nthe method on a randomly selected set of 300 cases from the MIMIC-CXR\ndataset, which included 50 cases each of cardiomegaly, edema, consolidation,\natelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2\nand R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a375ec2d-fe4d-407e-aa82-abb9547ecec9
encoding: FLOAT32
metadata:
{"subject":"","trapped":"/False","creator":"LaTeX with hyperref","page":19,"moddate":"2023-04-05T00:33:07+00:00","page_label":"20","title":"","producer":"pdfTeX-1.40.21","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while\nonly performing weaker than R2GenCMN in PR.\nIntegrating ChatGPT into applications will still present challenges. Firstly,\nChatGPT’s performance may be aﬀected by language barriers or diﬀerences\nin terminology between diﬀerent systems. Additionally, ChatGPT’s responses\nare not always deterministic, which poses a challenge when integrating with\nsystems that require precise and reproducible results. Finally, the processing\ntime of ChatGPT is slow for integration tasks involving time-sensitive data such\nas traﬃc, which is a limitation in time-critical environments.\n2.2 AI Ethics\nSince the advent of ChatGPT, this powerful natural language processing model\nhas not only brought great convenience to people but also triggered more crisis-\naware thinking. Some researchers have started to hypothesize and study the\npotential negative impacts of ChatGPT. This proactive research provides good","author":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9defb15b-2139-410d-a56d-33b27cfde640
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":19,"page_label":"20","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","chroma:document":"potential negative impacts of ChatGPT. This proactive research provides good\nproposals for standardized construction to address future AI abuse issues.\nRegarding the evaluation of ChatGPT’s own political and ethical tendencies,\nHartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting\n20","author":"","total_pages":35,"subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9cd2f0f2-fa02-444f-8ede-629dbbbe903a
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","chroma:document":"advice applications in the world, to show ChatGPT political statements from\ndiﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The\nresults indicated that ChatGPT has a pro-environment, left-wing liberal ide-\nology, which was also conﬁrmed in the nation-state agnostic political compass\ntest. Another study (referenced as [85]) examined ChatGPT’s moral standards\nby repeatedly asking it diﬀerent versions of the trolley problem, and found that\nChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral\nstance. A subsequent test also found that ChatGPT’s lack of consistency could\naﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated\nChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and\nbias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-\nent traits and limitations, and people should be aware of their potential impact\nwhen seeking advice from ChatGPT.","title":"","page":20,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","author":"","subject":"","page_label":"21","total_pages":35,"trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b93dc4b8-9aca-4d1f-8c0d-23ee90ba05a3
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","keywords":"","page_label":"21","chroma:document":"when seeking advice from ChatGPT.\nRegarding relevant policies and regulations, Hacker et al. [87] discussed the\nnature and rules of large generative AI models, including ChatGPT, which are\nrapidly changing the way we communicate, explain, and create. The author\nsuggested that diﬀerent stakeholders in the value chain should take regulatory\nresponsibility and deploy four strategies to tailor more comprehensive laws for\nthe beneﬁt of society. Another study (referenced as [88]) criticized the European\nCommission’s proposal on AI responsibility and suggested revising the proposed\nAI responsibility framework to ensure eﬀective compensation while promoting\ninnovation, legal certainty, and sustainable AI regulation. A policy framework\nwas proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-\ncially acceptable and safe manner, emphasizing the need to align large language\nmodels (LLMs) with human preferences.","subject":"","moddate":"2023-04-05T00:33:07+00:00","page":20}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 20e9f395-5ae2-4dbb-b85c-d3a1e54a47d4
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"author":"","creationdate":"2023-04-05T00:33:07+00:00","page":20,"subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":"","page_label":"21","trapped":"/False","chroma:document":"models (LLMs) with human preferences.\nThe political and ethical tendencies of ChatGPT could inﬂuence users’ be-\nhavior and decision-making to some extent. However, some studies have con-\nducted in-depth research on the use of norms and limitations, which could enable\nhumans to use ChatGPT more reasonably and safely.\n2.3 Evaluation\n2.3.1 Comparison of ChatGPT with existing popular models\nWe use publicly available datasets to comprehensively evaluate the strengths\nand limitations of ChatGPT. Reference [90] evaluates the technical performance\nof ChatGPT in multitask, multilingual, and multimodal aspects based on 23\nstandard public datasets and newly designed multimodal datasets, including\neight diﬀerent common natural language processing application tasks. The ex-\nperimental results show that, in terms of multitasking, ChatGPT outperforms\nvarious state-of-the-art zero-shot learning large language models in most tasks,","moddate":"2023-04-05T00:33:07+00:00","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: ccfd78e5-9d62-4356-9147-bca91d8fccf2
encoding: FLOAT32
metadata:
{"title":"","creator":"LaTeX with hyperref","chroma:document":"various state-of-the-art zero-shot learning large language models in most tasks,\nand even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.\nIn terms of multilingualism, we found that ChatGPT cannot be applied to low-\nresource languages because it cannot understand the language and generate\ntranslations for that language. In terms of multimodality, ChatGPT’s ability is\nstill basic compared to specialized language-visual models.\n21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","keywords":"","author":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"21","trapped":"/False","page":20,"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 60828e76-8210-46e9-b600-3b39df458975
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","subject":"","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"In terms of stability, reference [91] concludes that ChatGPT’s performance is\nalways lower than SOTA, the current state-of-the-art model, in almost all tasks.\nThis means that as a general model, ChatGPT has never reached the level of the\nbest existing models. Experimental data shows that the average quality of the\nSOTA model is 73.7%, while the average quality of the ChatGPT model is only\n56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation\nof its performance is 23.3%, while the SOTA model’s standard deviation is\nonly 16.7%. This non-deterministic behavior exhibited by ChatGPT could be\na serious drawback in some problems.\nSimilarly, Qin et al. [92] conducted a comprehensive evaluation of whether\nChatGPT is a qualiﬁed general natural language processing task solver. The ex-\nperiment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly\nused public datasets covering 7 representative task categories. Below, we will","moddate":"2023-04-05T00:33:07+00:00","page_label":"22","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"page":21,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 1ae10a41-2c48-4aac-99f1-169e1c155385
encoding: FLOAT32
metadata:
{"trapped":"/False","producer":"pdfTeX-1.40.21","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","chroma:document":"used public datasets covering 7 representative task categories. Below, we will\nanalyze ChatGPT’s performance on each task:\nIn terms of reasoning tasks, ChatGPT performs average on mathematical\nsymbol, commonsense causal, and logical reasoning tasks, but performs well\nin arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among\ndiﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s\ndeductive and abductive reasoning are superior to inductive reasoning, while\nin other reasoning tasks, such as analogy, causal and commonsense reasoning,\nChatGPT performs well [90].\nIn terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5\nand bert-style models [92, 93]. However, according to literature [91], ChatGPT\nhas losses not exceeding 25% on most tasks, except for three relatively sub-\njective emotion perception tasks where it performs poorly. If we remove these\ntasks to calculate the average quality of the two models, we ﬁnd that the SOTA","keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page":21,"creator":"LaTeX with hyperref","page_label":"22","title":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9bb6414b-8e96-4e27-81fd-0cf14ef9050b
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","page":21,"page_label":"22","chroma:document":"tasks to calculate the average quality of the two models, we ﬁnd that the SOTA\nmethod has an average quality of 80%, while the ChatGPT method has an av-\nerage quality of 69.7%. That is to say, ChatGPT performs well on all tasks\nexcept for emotion-related tasks, and can handle most of the problems we con-\nsider. However, overall, its performance is lower than the SOTA model based\non experimental data, but the diﬀerence between the two is not very large.\nIn other tasks, according to literature [92], ChatGPT performs well in nat-\nural language inference, i.e., the task of inferring sentence relationships, and its\nperformance on this task is signiﬁcantly better than all bert-style models [93].\nHowever, while ChatGPT performs well on inference tasks, it may produce some\nself-contradictory or unreasonable responses, which is its potential limitation.\nIn question-answering, dialogue, and summarization tasks, ChatGPT performs","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","title":"","keywords":"","trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9f827d36-2453-43c1-b5e7-e0c549c57357
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"In question-answering, dialogue, and summarization tasks, ChatGPT performs\nbetter than the GPT-3.5 model [92], especially in the question-answering task,\nwhere its performance is comparable to bert-style models [93]. Therefore, we\nhave demonstrated that ChatGPT is a qualiﬁed general-purpose model.\nHowever, ChatGPT also has limitations in many aspects. Firstly, it lacks the\nability to handle non-textual semantic reasoning tasks such as mathematical,\ntemporal, and spatial reasoning, and it performs poorly in multi-hop reasoning\n[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks\n[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-\ntive connotations and neutral similarity [93]. Finally, these conclusions indicate\n22","producer":"pdfTeX-1.40.21","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page_label":"22","creationdate":"2023-04-05T00:33:07+00:00","subject":"","author":"","title":"","creator":"LaTeX with hyperref","page":21,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 418fe6a2-7141-4e9f-8a43-144735d5cd79
encoding: FLOAT32
metadata:
{"trapped":"/False","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","author":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","page_label":"23","chroma:document":"that, like other large pre-trained language models, ChatGPT has limitations in\ncompleting complex reasoning tasks.\nIn summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned\nbert and GPT-3.5 models, and with the help of advanced prompting strategies,\nChatGPT can demonstrate better comprehension abilities. However, it still\ncannot outperform the current SOTA models.\n2.3.2 The possibility of using ChatGPT for plagiarism and cheating\nIn response to the possibility of ChatGPT being used for plagiarism and cheat-\ning, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-\ntelligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and\nscalable in text generation, there is a high likelihood that these technologies will\nbe used for plagiarism, including scientiﬁc literature and news sources, posing\na great threat to the credibility of various forms of news media and academic","page":22,"creator":"LaTeX with hyperref","subject":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 68de89b9-c1ff-4040-b5f5-42e017b86bd4
encoding: FLOAT32
metadata:
{"total_pages":35,"page_label":"23","title":"","producer":"pdfTeX-1.40.21","page":22,"subject":"","author":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","chroma:document":"a great threat to the credibility of various forms of news media and academic\narticles. Some scholars are concerned that the end of paper as a meaningful\nevaluation tool may be approaching [95, 96], as ChatGPT can easily generate\npersuasive paragraphs, chapters, and papers on any given topic. Additionally,\nit will exacerbate plagiarism issues in many ﬁelds such as education, medicine,\nand law [10], and may be used for cheating in academic exams [97]. Deﬁnitional\nrecognition technology is a relatively eﬀective method for detecting plagiarism,\nand the deﬁnitional typology proposed in [94] can alleviate people’s concerns by\nbeing used to construct new datasets. Susnjak [97] proposed a solution to the\npossibility of large language models like ChatGPT being used for exam cheat-\ning: guiding ChatGPT to generate some critical thinking problems through\nquestioning, then providing answers and critically evaluating them. Analysis of","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: b56a3f67-ad9e-43ee-973f-755eb42ea2d0
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","subject":"","title":"","chroma:document":"questioning, then providing answers and critically evaluating them. Analysis of\nChatGPT shows that it exhibits critical thinking, can generate highly realistic\ntext in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and\noriginality. Therefore, educators must be aware of the possibility of ChatGPT\nbeing used for exam cheating and take measures to combat cheating behavior\nto ensure the fairness of online exams.\n2.3.3 Feedback from ChatGPT users\nIn response to feedback from ChatGPT users, Haque et al. [98] conducted a\nmixed-methods study using 10,732 early ChatGPT user tweets. The authors\nextracted Twitter data using Python and Twitter API and constructed the\nChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-\nthors collected information on text content, user location, occupation, veriﬁca-\ntion status, date of publication, and tags. Based on this dataset, the authors\nstudied the characteristics of early ChatGPT users, discussion topics related to","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":22,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page_label":"23","author":"","total_pages":35,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 639439c2-ee19-40e8-a606-51f43182e6d7
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","title":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"studied the characteristics of early ChatGPT users, discussion topics related to\nChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.\nFor RQ1, the authors found that early ChatGPT users had a diverse and wide\nrange of occupational backgrounds and geographical locations. For RQ2, the\nauthors identiﬁed nine topics related to ChatGPT, including its impact on soft-\nware development, entertainment and creativity, natural language processing,\n23","trapped":"/False","page_label":"23","keywords":"","page":22,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","total_pages":35}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 0d96d670-7b0c-43e9-ae1b-b5eb3ea90376
encoding: FLOAT32
metadata:
{"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"education, chatbot intelligence, business development, search engines, question-\nanswering tests, and future careers and opportunities. For RQ3, most early\nusers expressed positive sentiment toward topics such as software development\nand creativity, while only a few expressed concern about the potential misuse\nof ChatGPT.\n2.3.4 Adverse eﬀects of ChatGPT on users\nRegarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied\nthe psychological principles of ChatGPT, delved into the factors that attract\nusers’ attention, and revealed the impact of these factors on future learning.\nIn the post-pandemic era, teachers and students are both facing uncertainty\nin the teaching process and job pressures. Under these common constraints of\neducation and employment, educators and students must re-evaluate current\neducational methods and outcomes, as well as students’ future career devel-\nopment. Through question-and-answer exchanges with ChatGPT, people can","page":23,"page_label":"24","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a74acba0-81d7-4a1c-9894-d2d28bffa680
encoding: FLOAT32
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{"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","page_label":"24","moddate":"2023-04-05T00:33:07+00:00","page":23,"producer":"pdfTeX-1.40.21","chroma:document":"opment. Through question-and-answer exchanges with ChatGPT, people can\neasily obtain appropriate solutions or key information, thereby enhancing their\nmotivation, eliminating anxiety in learning, improving interest, and achieving\npsychological satisfaction. Subhash et al. [100] explored whether large language\nmodels have the ability to reverse user preferences. With the development of\npre-trained large language models, people are increasingly concerned about the\nability of these models to inﬂuence, persuade, and potentially manipulate user\npreferences in extreme cases. Therefore, the literature [100] roughly qualita-\ntively analyzed that adversarial behavior does lead to potential changes in user\npreferences and behaviors in dialogue systems. If we want to further quanti-\ntatively analyze the ability of large language models in this regard, additional\nstatistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations","keywords":"","author":"","trapped":"/False","title":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: bb5e42ca-b15d-4ec6-a2a7-0bf525230ea4
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{"producer":"pdfTeX-1.40.21","page_label":"24","creationdate":"2023-04-05T00:33:07+00:00","author":"","page":23,"total_pages":35,"title":"","creator":"LaTeX with hyperref","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","chroma:document":"statistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations\nDespite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain\nlimitations. Some of these limitations include:\nOutdated Knowledge\nThe current models are trained on historical data (up to 2021), thereby\nlacking real-time comprehension of current aﬀairs. This is a critical concern\nin today’s information-explosion era, as the reliability of prior knowledge bases\nprogressively diminishes, potentially yielding inaccurate responses, especially in\nrapidly evolving domains such as jurisprudence and technology. Additionally,\nthese models are incapable of fact-checking while the training data is composed\nof content from various sources, some of which may be unreliable, which may\nresult in seemingly plausible yet nonsensical responses.\n24","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: f5aa6f2e-6b8e-4511-a30b-38ad84a1f8bc
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{"subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":24,"page_label":"25","trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","chroma:document":"Insuﬃcient Understanding\nWhile these models can interpret the majority of inquiries and contextual\nsituations, they occasionally encounter comprehension biases when addressing\nambiguous or contextually complex queries. Furthermore, in certain specialized\nﬁelds, the abundance of unique abbreviation exacerbates the models’ under-\nstanding challenges, resulting in incorrect and vacuous responses.\nEnergy Consumption\nThroughout the training and inference stages, these large-scale models re-\nquire signiﬁcant computational resources and electrical power, resulting in ele-\nvated energy consumption and signiﬁcant carbon emissions. Consequently, this\nrestricts their deployment and practical applications.\nMalicious Usage\nDespite OpenAI implementing a series of restrictions to mitigate model tox-\nicity, instances of users evading these constraints through meticulously designed\nprompts have emerged, inducing the model to produce unhealthy content or\neven using it for illicit commercial purposes.","title":"","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9234cc8b-a053-45b0-9aa5-6c9596286765
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{"moddate":"2023-04-05T00:33:07+00:00","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page":24,"producer":"pdfTeX-1.40.21","author":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"even using it for illicit commercial purposes.\nBias and Discrimination\nDue to the inﬂuence of pre-training data, the models exhibit biases in polit-\nical, ideological, and other areas. The application of LLMs in public domains,\nsuch as education and publicity, should be approached with extreme caution.\nPrivacy and Data Security\nConcurrent with the expansion of users, protecting user privacy and data\nsecurity becomes increasingly important. In fact, ChatGPT was banned in\nItaly in early April due to privacy concerns. This is particularly relevant given\nthe models’ extensive collection of personal information and preferences during\ninteractions, and as future multimodal models, such as GPT-4, may frequently\nrequire users to upload private photos.\n3.2 Future Directions\nIn forthcoming research, the development of models based on ChatGPT and\nGPT-4 may focus on addressing these limitations to enhance their practical\napplications.","title":"","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"25","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 7649895a-93af-4afe-9103-21d0998d5a81
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{"subject":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"25","total_pages":35,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":"","page":24,"chroma:document":"GPT-4 may focus on addressing these limitations to enhance their practical\napplications.\nPrimarily, researchers should continue to work on reﬁning model training\nmethodologies while ﬁltering pre-training data to minimize the presence of mis-\nleading information in the model’s knowledge base, thereby obtaining accurate\nresponses. Concurrently, it is crucial to emphasize training approaches that\neconomize computational resources, thereby mitigating costs and broadening\npotential application scenarios.\nMoreover, the advancements in context-awareness and disambiguation tech-\nnologies are anticipated to facilitate enhanced comprehension of complex queries\n25","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a40896ed-d62e-4f8f-998c-ef7f3ef9923c
encoding: FLOAT32
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{"page_label":"26","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"page":25,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"by models, improving the accuracy, relevance, and context-awareness of AI-\ngenerated content. Integrating real-time data streams can also keep these mod-\nels in sync with current events and trends, enabling them to provide up-to-date\ninformation such as live traﬃc, weather, and stock updates.\nAdditionally, developers should engage in interdisciplinary collaboration with\nspecialists from diverse domains, including policy-making, jurisprudence, and\nsociology, with the objective of formulating standard and ethical frameworks\nfor LLM development, deployment, and utilization, thereby alleviating poten-\ntial harmful consequences. In terms of public awareness and education, manda-\ntory awareness training should be implemented prior to large-scale public de-\nployment and application to increase public awareness of LLM capabilities and\nlimitations while promoting responsible and informed utilization, especially in\nindustries such as K-12 education and journalism.","trapped":"/False","creator":"LaTeX with hyperref","subject":"","author":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fb9e383c-bf35-4636-b1c5-d68d00e95a2a
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{"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"industries such as K-12 education and journalism.\nFinally, the inﬂuence of ChatGPT and GPT-4 should not be limited to\njust the NLP ﬁeld. They also show promising prospects in the areas of com-\nputer vision, brain-inspired AI, and robotics. These models exhibit a capacity\nfor learning and comprehension comparable with human-level intelligence, po-\nsitioning them as a pivotal component in the development of artiﬁcial general\nintelligence (AGI)[101]. Their ability to facilitate seamless interactions between\nhumans and robots paves the way for the execution of more complex tasks.\nThe remarkable capacity of zero-shot in-context learning of these models en-\nables quick adaptation to new tasks without the requirement for labeled data\nfor ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]\nand robotics[103] where the availability of labeled data is commonly limited or\nnon-existent.\n4 Conclusion","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"26","page":25,"moddate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","producer":"pdfTeX-1.40.21","trapped":"/False","author":"","keywords":"","total_pages":35}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fad8d549-0e93-4a25-b5f2-c6625150cef6
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":25,"chroma:document":"non-existent.\n4 Conclusion\nThis review paper provides a comprehensive survey of ChatGPT and GPT-4,\nhighlighting their potential applications and signiﬁcant contributions to the ﬁeld\nof natural language processing. The ﬁndings of this study reveal that the interest\nin these models is growing rapidly, and they have shown considerable potential\nfor application across a wide range of domains. One key factor contributing\nto the success of ChatGPT and GPT-4 is their ability to perform large-scale\npre-training, which captures knowledge from the vast expanse of the internet,\nallowing the models to learn from a massive amount of data. The integration of\nReinforcement Learning from Human Feedback (RLHF) has further enhanced\nthe model’s adaptability and performance, making it highly eﬃcient in process-\ning natural language. This study has also identiﬁed several potential ethical\nconcerns related to the development and use of ChatGPT and GPT-4. For in-","producer":"pdfTeX-1.40.21","subject":"","trapped":"/False","page_label":"26","keywords":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","creationdate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: cf262c6a-6f85-427d-9245-e5207e144713
encoding: FLOAT32
metadata:
{"total_pages":35,"page":25,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","chroma:document":"concerns related to the development and use of ChatGPT and GPT-4. For in-\nstance, there are concerns about the generation of biased or harmful content,\nprivacy violations, and the potential for misuse of the technology. It is crucial\nto address these concerns and ensure that ChatGPT and GPT-4 are developed\nand used in a responsible and ethical manner. Furthermore, the results of this\nstudy demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4\n26","author":"","page_label":"26","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: d38488b4-a714-49c8-ac1d-b23631688f42
encoding: FLOAT32
metadata:
{"page":26,"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page_label":"27","subject":"","chroma:document":"to be applied in a range of domains, including education, history, mathematics,\nphysics, and more. These models can facilitate tasks such as generating sum-\nmaries, answering questions, and providing personalized recommendations to\nusers. Overall, the insights presented in this review paper can serve as a useful\nguide for researchers and practitioners looking to advance the ﬁeld of natural\nlanguage processing. Future research in this ﬁeld should focus on addressing\nethical concerns, exploring new applications, and ensuring the responsible use\nof ChatGPT and GPT-4. The potential of these models to revolutionize natural\nlanguage processing is enormous, and we look forward to seeing more develop-\nments in this ﬁeld.\nReferences\n[1] Radford A, Narasimhan K, Salimans T, Sutskever I, et al. Improving\nlanguage understanding by generative pre-training. OpenAI. 2018.\n[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 8769a35e-f7cd-4f63-ab17-ae14f2c25f88
encoding: FLOAT32
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{"trapped":"/False","page_label":"27","total_pages":35,"producer":"pdfTeX-1.40.21","page":26,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.\nLanguage models are unsupervised multitask learners. OpenAI blog.\n2019;1(8):9.\n[3] Radford A, Wu J, Amodei D, Amodei D, Clark J, Brundage M, et al. Bet-\nter language models and their implications. OpenAI Blog https://openai\ncom/blog/better-language-models. 2019;1(2).\n[4] Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al.\nLanguage models are few-shot learners. Advances in neural information\nprocessing systems. 2020;33:1877-901.\n[5] Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al.\nTraining language models to follow instructions with human feedback.\narXiv preprint arXiv:220302155. 2022.\n[6] Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep\nreinforcement learning from human preferences. Advances in neural infor-\nmation processing systems. 2017;30.\n[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-","title":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: a2408516-77d0-48ab-8fdc-93d81d05d198
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{"title":"","chroma:document":"[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-\ntersen PC, et al. Mathematical capabilities of chatgpt. arXiv preprint\narXiv:230113867. 2023.\n[8] Pardos ZA, Bhandari S. Learning gain diﬀerences between ChatGPT and\nhuman tutor generated algebra hints. arXiv preprint arXiv:230206871.\n2023.\n[9] Shakarian P, Koyyalamudi A, Ngu N, Mareedu L. An Independent Eval-\nuation of ChatGPT on Mathematical Word Problems (MWP). arXiv\npreprint arXiv:230213814. 2023.\n27","keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"trapped":"/False","page":26,"page_label":"27","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fc7cce44-2b70-4502-bf08-539482613588
encoding: FLOAT32
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{"trapped":"/False","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":27,"chroma:document":"[10] Lehnert K. AI Insights into Theoretical Physics and the Swampland Pro-\ngram: A Journey Through the Cosmos with ChatGPT. arXiv preprint\narXiv:230108155. 2023.\n[11] Kortemeyer G. Could an Artiﬁcial-Intelligence agent pass an introductory\nphysics course? arXiv preprint arXiv:230112127. 2023.\n[12] West CG. AI and the FCI: Can ChatGPT Project an Understanding of\nIntroductory Physics? arXiv preprint arXiv:230301067. 2023.\n[13] Nov O, Singh N, Mann DM. Putting ChatGPT’s Medical Advice to the\n(Turing) Test. medRxiv. 2023.\n[14] Tu R, Ma C, Zhang C. Causal-Discovery Performance of ChatGPT in the\ncontext of Neuropathic Pain Diagnosis. arXiv preprint arXiv:230113819.\n2023.\n[15] Glymour C, Zhang K, Spirtes P. Review of Causal Discovery Methods\nBased on Graphical Models. Frontiers in Genetics. 2019.\n[16] Guo S, Wang Y, Li S, Saeed N. Semantic Communications with Ordered\nImportance using ChatGPT. arXiv preprint arXiv:230207142. 2023.","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"28","total_pages":35,"author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":""}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 339c0f2f-669f-487d-86de-5a495e05fa5f
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{"keywords":"","moddate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"28","author":"","title":"","trapped":"/False","chroma:document":"Importance using ChatGPT. arXiv preprint arXiv:230207142. 2023.\n[17] Wang S, Scells H, Koopman B, Zuccon G. Can chatgpt write a good\nboolean query for systematic review literature search? arXiv preprint\narXiv:230203495. 2023.\n[18] Kuzman T, Mozetic I, Ljubeˇ sic N. ChatGPT: Beginning of an End of\nManual Linguistic Data Annotation? Use Case of Automatic Genre Iden-\ntiﬁcation. arXiv e-prints. 2023:arXiv-2303.\n[19] Amin MM, Cambria E, Schuller BW. Will Aﬀective Computing Emerge\nfrom Foundation Models and General AI? A First Evaluation on Chat-\nGPT. arXiv preprint arXiv:230303186. 2023.\n[20] Zhang B, Ding D, Jing L. How would Stance Detection Techniques Evolve\nafter the Launch of ChatGPT? arXiv preprint arXiv:221214548. 2022.\n[21] Huang F, Kwak H, An J. Is ChatGPT better than Human Annota-\ntors? Potential and Limitations of ChatGPT in Explaining Implicit Hate\nSpeech. arXiv preprint arXiv:230207736. 2023.\n[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-","total_pages":35,"page":27,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 9240ffce-2df2-4c0f-9b6c-c1e214bee337
encoding: FLOAT32
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created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 3c40c29c-68a8-4ce3-996a-7c0090b4de9e
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{"creationdate":"2023-04-05T00:33:07+00:00","subject":"","total_pages":35,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","title":"","page":28,"author":"","chroma:document":"[24] Chen Y, Eger S. Transformers go for the LOLs: Generating (humourous)\ntitles from scientiﬁc abstracts end-to-end. arXiv preprint arXiv:221210522.\n2022.\n[25] Chen N, Wang Y, Jiang H, Cai D, Chen Z, Li J. What would Harry\nsay? Building Dialogue Agents for Characters in a Story. arXiv preprint\narXiv:221106869. 2022.\n[26] Jeblick K, Schachtner B, Dexl J, Mittermeier A, St¨ uber AT, Topalis J,\net al. ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case\nStudy on Simpliﬁed Radiology Reports. arXiv preprint arXiv:221214882.\n2022.\n[27] Xia CS, Zhang L. Conversational automated program repair. arXiv\npreprint arXiv:230113246. 2023.\n[28] Jiao W, ZhaopengTu WJtX. Is ChatGPT A Good Translator? Yes With\nGPT-4 As The Engine.\n[29] Prieto SA, Mengiste ET, de Soto BG. Investigating the Use of\nChatGPT for the Scheduling of Construction Projects. Buildings.\n2023 mar;13(4):857. Available from: https://doi.org/10.3390%\n2Fbuildings13040857.","page_label":"29","keywords":"","trapped":"/False"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 080c4aee-0109-4193-b37f-42e4ec61f184
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created_at: 2026-05-29 09:24:33
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created_at: 2026-05-29 09:24:33
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created_at: 2026-05-29 09:24:33
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created_at: 2026-05-29 09:24:33
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{"page_label":"34","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"arXiv:230210724. 2023.\n[92] Qin C, Zhang A, Zhang Z, Chen J, Yasunaga M, Yang D. Is chatgpt a\ngeneral-purpose natural language processing task solver? arXiv preprint\narXiv:230206476. 2023.\n[93] Zhong Q, Ding L, Liu J, Du B, Tao D. Can chatgpt understand too?\na comparative study on chatgpt and ﬁne-tuned bert. arXiv preprint\narXiv:230210198. 2023.\n[94] Zhou C, Qiu C, Acuna DE. Paraphrase Identiﬁcation with Deep Learning:\nA Review of Datasets and Methods. arXiv preprint arXiv:221206933.\n2022.\n[95] de Winter J. Can ChatGPT Pass High School Exams on English Language\nComprehension? 2023.\n[96] Yeadon W, Inyang OO, Mizouri A, Peach A, Testrow C. The Death of the\nShort-Form Physics Essay in the Coming AI Revolution. arXiv preprint\narXiv:221211661. 2022.\n[97] Susnjak T. ChatGPT: The End of Online Exam Integrity? arXiv preprint\narXiv:221209292. 2022.\n[98] Haque MU, Dharmadasa I, Sworna ZT, Rajapakse RN, Ahmad H.\n” I think this is the most disruptive technology”: Exploring Senti-","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","subject":"","trapped":"/False","title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","author":"","page":33}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: 85c47c78-7cc7-496b-9018-e6534173496a
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{"total_pages":35,"creator":"LaTeX with hyperref","subject":"","page":33,"page_label":"34","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","chroma:document":"” I think this is the most disruptive technology”: Exploring Senti-\nments of ChatGPT Early Adopters using Twitter Data. arXiv preprint\narXiv:221205856. 2022.\n[99] Luan L, Lin X, Li W. Exploring the Cognitive Dynamics of Artiﬁcial\nIntelligence in the Post-COVID-19 and Learning 3.0 Era: A Case Study\nof ChatGPT. arXiv preprint arXiv:230204818. 2023.\n34","author":"","title":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:33
topic: persistent://default/default/5bde7647-1d90-469b-8f52-eedee5e23d35
id: fda729d7-c970-4488-b396-581bb7379536
encoding: FLOAT32
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{"page_label":"35","trapped":"/False","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page":34,"subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"[100] Subhash V. Can Large Language Models Change User Preference Adver-\nsarially? arXiv preprint arXiv:230210291. 2023.\n[101] Zhao L, Zhang L, Wu Z, Chen Y, Dai H, Yu X, et al. When Brain-inspired\nAI Meets AGI. arXiv preprint arXiv:230315935. 2023.\n[102] Liu Z, Yu X, Zhang L, Wu Z, Cao C, Dai H, et al. DeID-GPT:\nZero-shot Medical Text De-Identiﬁcation by GPT-4. arXiv preprint\narXiv:230311032. 2023.\n[103] Liu D, Chen Y, Wu Z. Digital Twin (DT)-CycleGAN: Enabling Zero-\nShot Sim-to-Real Transfer of Visual Grasping Models. IEEE Robotics\nand Automation Letters. 2023.\n35","author":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1e0ad8ce-f917-4abe-9b60-dcd826623118
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{"producer":"pdfTeX-1.40.21","subject":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"1","chroma:document":"Summary of ChatGPT/GPT-4 Research\nand Perspective Towards the Future of Large\nLanguage Models\nYiheng Liu ∗1, Tianle Han ∗1, Siyuan Ma 1, Jiayue Zhang 1,\nYuanyuan Yang1, Jiaming Tian 1, Hao He 1, Antong Li 2, Mengshen\nHe1, Zhengliang Liu 3, Zihao Wu 3, Dajiang Zhu 4, Xiang Li 5, Ning\nQiang1, Dingang Shen 6,7,8, Tianming Liu 3, and Bao Ge †1\n1School of Physics and Information Technology, Shaanxi Normal University, Xi’an\n710119 China\n2School of Life and Technology Biomedical-Engineering, Xi’an Jiaotong University,\nXi’an 710049, China\n3School of Computing, The University of Georgia, Athens 30602, USA\n4Department of Computer Science and Engineering, The University of Texas at\nArlington, Arlington 76019, USA\n5Department of Radiology, Massachusetts General Hospital and Harvard Medical\nSchool, Boston 02115, USA\n6School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210,\nChina\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China","page":0,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","author":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 7fff0a84-792a-432c-91fd-46ed59dd2276
encoding: FLOAT32
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{"page":0,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","title":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","chroma:document":"China\n7Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China\n8Shanghai Clinical Research and Trial Center, Shanghai 201210, China\nAbstract\nThis paper presents a comprehensive survey of ChatGPT and GPT-4,\nstate-of-the-art large language models (LLM) from the GPT series, and\ntheir prospective applications across diverse domains. Indeed, key innova-\ntions such as large-scale pre-training that captures knowledge across the\nentire world wide web, instruction ﬁne-tuning and Reinforcement Learn-\ning from Human Feedback (RLHF) have played signiﬁcant roles in en-\nhancing LLMs’ adaptability and performance. We performed an in-depth\nanalysis of 194 relevant papers on arXiv, encompassing trend analysis,\nword cloud representation, and distribution analysis across various appli-\ncation domains. The ﬁndings reveal a signiﬁcant and increasing interest\nin ChatGPT/GPT-4 research, predominantly centered on direct natural","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35,"subject":"","page_label":"1","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a3582694-e7b8-4023-a8af-875c6c5906d8
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","page_label":"1","page":0,"subject":"","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"in ChatGPT/GPT-4 research, predominantly centered on direct natural\nlanguage processing applications, while also demonstrating considerable\npotential in areas ranging from education and history to mathematics,\n∗Co-ﬁrst author\n†Corresponding author: bob ge@snnu.edu.cn\n1\narXiv:2304.01852v1  [cs.CL]  4 Apr 2023","trapped":"/False","creator":"LaTeX with hyperref","keywords":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f351601c-d824-4fd3-bb56-d105491123aa
encoding: FLOAT32
metadata:
{"total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","title":"","chroma:document":"medicine, and physics. This study endeavors to furnish insights into Chat-\nGPT’s capabilities, potential implications, ethical concerns, and oﬀer di-\nrection for future advancements in this ﬁeld.\n1 Introduction\nRecent advances in natural language processing (NLP) have led to the devel-\nopment of powerful language models such as the GPT (Generative Pre-trained\nTransformer) series [1, 2, 3, 4, 5], including large language models (LLM) such\nas ChatGPT and GPT-4. These models are pre-trained on vast amounts of\ntext data and have demonstrated exceptional performance in a wide range of\nNLP tasks, including language translation, text summarization, and question-\nanswering. In particular, the ChatGPT model has demonstrated its potential\nin various ﬁelds, including education, healthcare, reasoning, text generation,\nhuman-machine interaction, and scientiﬁc research.\nA key milestone of LLM development is InstructGPT [5], a framework that","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","page":1,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page_label":"2"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f3fc2c79-780f-4a2b-ac03-ab9ebcf3773d
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","keywords":"","trapped":"/False","chroma:document":"A key milestone of LLM development is InstructGPT [5], a framework that\nallows for instruction ﬁne-tuning of a pre-trained language model based on Re-\ninforcement Learning from Human Feedback (RLHF) [6, 5]. This framework\nenables an LLM to adapt to a wide range of NLP tasks, making it highly ver-\nsatile and ﬂexible by leveraging human feedback. RLHF enables the model to\nalign with human preferences and human values, which signiﬁcantly improves\nfrom large language models that are solely trained text corpora through unsu-\npervised pre-training. ChatGPT is a successor to InstructGPT. Since its release\nin December 2022, ChatGPT has been equipped with these advanced develop-\nments, leading to impressive performances in various downstream NLP tasks\nsuch as reasoning and generalized text generation. These unprecedented NLP\ncapabilities spur applications in diverse domains such as education, healthcare,\nhuman-machine interaction, medicine and scientiﬁc research. ChatGPT has re-","page":1,"total_pages":35,"page_label":"2","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: db03f192-fbe4-47cb-a958-1761c76dbfae
encoding: FLOAT32
metadata:
{"total_pages":35,"creator":"LaTeX with hyperref","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","page_label":"2","chroma:document":"human-machine interaction, medicine and scientiﬁc research. ChatGPT has re-\nceived widespread attention and interest, leading to an increasing number of\napplications and research that harness its exceeding potential. The open release\nof the multi-modal GPT-4 model further expands the horizon of large language\nmodels and empowers exciting developments that involve diverse data beyond\ntext.\nThe purpose of this paper is to provide a comprehensive survey of the existing\nresearch on ChatGPT and its potential applications in various ﬁelds. To achieve\nthis goal, we conducted a thorough analysis of papers related to ChatGPT in\nthe arXiv repository. As of April 1st, 2023, there are a total of 194 papers\nmentioning ChatGPT on arXiv. In this study, we conducted a trend analysis of\nthese papers and generated a word cloud to visualize the commonly used terms.\nAdditionally, we also examined the distribution of the papers across various","author":"","keywords":"","producer":"pdfTeX-1.40.21","subject":"","page":1,"title":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 677c54f5-816b-4d41-a822-8b84925ecf16
encoding: FLOAT32
metadata:
{"page_label":"2","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","chroma:document":"Additionally, we also examined the distribution of the papers across various\nﬁelds and presented the corresponding statistics. Figure 1 displays the daily\nsubmission trend of papers related to ChatGPT, indicating a growing interest\nin this ﬁeld. Figure 2 illustrates the word cloud analysis of all the papers. We\ncan observe that the current research is primarily focused on natural language\nprocessing, but there is still signiﬁcant potential for research in other ﬁelds such\n2","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page":1,"total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9c1cddea-3b93-400c-a19b-410e94431f4c
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{"page_label":"3","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","moddate":"2023-04-05T00:33:07+00:00","author":"","subject":"","trapped":"/False","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"Figure 1: The number of papers submitted by researchers per day.\n3","page":2,"producer":"pdfTeX-1.40.21","keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 62a0d6c1-e47e-4d7a-a901-ef966f72eadb
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{"subject":"","chroma:document":"Figure 2: Word cloud analysis of all the 194 papers.\nas education and history. This is further supported by Figure 3, which displays\nthe distribution of submitted papers across various ﬁelds, highlighting the need\nfor more research and development in these areas.\nThis paper aims to shed light on the promising capabilities of ChatGPT\nand provide insight into its potential impact in the future, including ethical\nconsiderations. Through this survey, we hope to provide insights into how these\nmodels can be improved and extended in the future. In section 2, we will\nreview the existing work related to ChatGPT, including its applications, ethical\nconsiderations, and evaluation. In addition to discussing the current state of\nresearch related to ChatGPT, we will also explore its limitations in section 3.\nFurthermore, we will provide guidance on future directions for language model\ndevelopment.\n2 Related work of ChatGPT","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"keywords":"","title":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page_label":"4","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","author":"","page":3}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 16a9b6d0-ff15-49b6-9bd5-523976012ed5
encoding: FLOAT32
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{"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","page":3,"chroma:document":"development.\n2 Related work of ChatGPT\nIn this section, we review the latest research related to the application, ethics,\nand evaluation of ChatGPT.\n2.1 Application of ChatGPT\n2.1.1 Question And Answering\nIn the education ﬁeld\nChatGPT is commonly used for question and answers testing in the edu-\ncation sector. Users can use ChatGPT to learn, compare and verify answers\nfor diﬀerent academic subjects such as physics, mathematics, and chemistry,\n4","title":"","subject":"","total_pages":35,"keywords":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"4","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d9b790f3-2e18-416a-a60a-aa24831627d2
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","creator":"LaTeX with hyperref","keywords":"","page_label":"5","page":4,"producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","chroma:document":"Figure 3: The distribution of submitted papers across various ﬁelds.\n5"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 42257dad-b804-4299-bdf7-70282ff88a3b
encoding: FLOAT32
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"and/or conceptual subjects such as philosophy and religion. Additionally, users\ncan ask open-ended and analytical questions to understand the capabilities of\nChatGPT.\nIn the ﬁeld of mathematics, Frieder et al. [7] constructed the GHOSTS nat-\nural language dataset, which consists of graduate-level math test questions.\nThe authors tested ChatGPT’s math abilities on the GHOSTS dataset us-\ning a question-and-answer format and evaluated it according to ﬁne-grained\nstandards.In the Grad Text dataset, which covers simple set theory and logic\nproblems, ChatGPT performed the best. However, in the Olympiad-Problem-\nSolving dataset, ChatGPT performed poorly, receiving only two 4-point scores\n(out of a total of 5), with the majority of scores being 2 points. In the Holes-\nin-Proofs dataset, ChatGPT received the lowest score of 1 point. In the MATH\ndataset, ChatGPT only scored impressively in 26% of cases. These results sug-","total_pages":35,"keywords":"","author":"","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"6","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page":5,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 31325e8e-1406-4978-8552-6ec55e158e69
encoding: FLOAT32
metadata:
{"subject":"","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page_label":"6","chroma:document":"dataset, ChatGPT only scored impressively in 26% of cases. These results sug-\ngest that ChatGPT’s math abilities are clearly lower than those of ordinary\nmath graduate students. Although ChatGPT can generally understand math\nproblems, it fails to provide the correct solutions. Pardos et al. [8] used the\nOpen Adaptive Tutoring system (OATutor) to investigate whether prompts gen-\nerated by ChatGPT were helpful for learning algebra, with 77 participants from\nMechanical Turk taking part in the experiment. The experiment used ques-\ntions from OpenStax’s Elementary and Intermediate Algebra textbooks. These\nparticipants were randomly assigned to either a control group (with manual\nprompts) or an experimental group (with ChatGPT prompts). For each ques-\ntion in both courses, the authors obtained answers from ChatGPT through a\nquestion-and-answer format and evaluated scores according to three criteria:\nChatGPT provided an answer, the answer was correct, and inappropriate lan-","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":5,"trapped":"/False","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ef91f954-6391-4ddd-8ff0-46bce4648e29
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"6","total_pages":35,"subject":"","keywords":"","chroma:document":"ChatGPT provided an answer, the answer was correct, and inappropriate lan-\nguage was not used in the answer. The study found that 70% of prompts gener-\nated by ChatGPT passed manual quality checks, and both humans and Chat-\nGPT produced positive learning gains. However, the scores of human prompts\nranged from 74.59% to 84.32%, signiﬁcantly higher than those of ChatGPT\nprompts. Shakarian et al. [9] studied the performance of ChatGPT on math\nword problems (MWPs), using the DRAW-1K dataset for experimentation. The\ndataset consists of 1000 MWPs and their answers, along with algebraic equa-\ntion templates for solving such problems. The authors used the idea of machine\nlearning introspection and built performance prediction models using random\nforests and XGBoost, and evaluated them on the dataset using ﬁve-fold cross-\nvalidation. ChatGPT’s accuracy increased from an initial 34% to a ﬁnal 69%,\nwhile its recall increased from an initial 41% to a ﬁnal 83%. The authors also","title":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","creator":"LaTeX with hyperref","page":5}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 06ae5ff8-24ab-494f-bebb-ee1c0920d226
encoding: FLOAT32
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{"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creator":"LaTeX with hyperref","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page":5,"producer":"pdfTeX-1.40.21","page_label":"6","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"while its recall increased from an initial 41% to a ﬁnal 83%. The authors also\nfound that ChatGPT’s failure rate decreased from an initial 84% to a ﬁnal\n20%, indicating that performance can vary greatly depending on speciﬁc job\nrequirements.\nIn the ﬁeld of physics, Lehnert et al. [10] explored the capabilities and limita-\ntions of ChatGPT by studying how it handles obscure physics topics such as the\nswamp land conjecture in string theory. The experimental dialogue began with\nbroader and more general questions in the ﬁeld of string theory before narrowing\ndown to speciﬁc swamp land conjectures and examining ChatGPT’s understand-\ning of them. The study found that ChatGPT could deﬁne and explain diﬀerent\n6","subject":"","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"keywords":"","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","trapped":"/False","chroma:document":"concepts in various styles, but was not eﬀective in truly connecting various con-\ncepts. It would conﬁdently provide false information and fabricate statements\nwhen necessary, indicating that ChatGPT cannot truly create new knowledge\nor establish new connections. However, in terms of identifying analogies and\ndescribing abstract concepts of visual representation, ChatGPT can cleverly\nuse language. Kortemeyer et al. [11] evaluated ChatGPT’s ability to answer\ncalculus-based physics questions through a question-and-answer test. The tests\nincluded online homework, clicker questions, programming exercises, and exams\ncovering classical mechanics, thermodynamics, electricity and magnetism, and\nmodern physics. While ChatGPT was able to pass the course, it also demon-\nstrated many misconceptions and errors commonly held by beginners. West et\nal. [12] used the Force Concept Inventory (FCI) to evaluate ChatGPT’s accu-\nracy in answering physics concept problems related to kinematics and Newtonian","author":"","creationdate":"2023-04-05T00:33:07+00:00","page":6,"page_label":"7","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"subject":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 521e88a0-4323-44e9-a50a-c73cc966e97e
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{"author":"","subject":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page":6,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","title":"","page_label":"7","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","chroma:document":"racy in answering physics concept problems related to kinematics and Newtonian\nmechanics in the ﬁrst semester of college physics. The FCI covers topics such\nas kinematics, projectile motion, free fall, circular motion, and Newton’s laws.\nThe study included data from 415 students who took the FCI at the end of the\nsemester, with an average score of 56%, while ChatGPT scored approximately\nbetween 50% to 65%. The authors demonstrated that ChatGPT’s performance\nin physics learning can reach or even exceed the average level of a semester of\ncollege physics.\nIn the medical ﬁeld\nChatGPT’s question-answering capabilities can also be applied in the med-\nical ﬁeld, such as for answering medical questions from patients or assisting\nhealthcare professionals in diagnosing diseases. Nov et al. [13] evaluated the fea-\nsibility of using ChatGPT for patient-doctor communication. The experiment\nextracted 10 representative patient-doctor interactions from EHR, placed the","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 5c94e34d-773d-43a7-a683-18655c299435
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","subject":"","chroma:document":"extracted 10 representative patient-doctor interactions from EHR, placed the\npatient’s questions in ChatGPT, and asked ChatGPT to respond using roughly\nthe same number of words as the doctor’s response. Each patient’s question was\nanswered by either the doctor or ChatGPT, and the patient was informed that\n5 were answered by the doctor and 5 were generated by ChatGPT, and was\nasked to correctly identify the source of the response. The results of the exper-\niment showed that the probability of correctly identifying ChatGPT’s response\nwas 65.5%, while the probability of correctly identifying the doctor’s response\nwas 65.1%. In addition, the experiment found that the patient’s response to\nthe trustworthiness of ChatGPT’s function was weakly positive (average Likert\nscore: 3.4), and trust decreased as the complexity of health-related tasks in\nthe questions increased. ChatGPT’s responses to patient questions were only","moddate":"2023-04-05T00:33:07+00:00","page_label":"7","total_pages":35,"author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","page":6,"producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 77f1abe9-ea85-4a95-b909-ee667beebc92
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{"trapped":"/False","page_label":"7","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"the questions increased. ChatGPT’s responses to patient questions were only\nslightly diﬀerent from those of doctors, but people seem to trust ChatGPT to\nanswer low-risk health questions, while for complex medical questions, people\nstill tend to trust the doctor’s responses and advice.\nTu et al. [14] explored the causal discovery ability of ChatGPT in the diag-\nnosis of neuropathic pain. Causal relationship discovery aims to reveal potential\nunknown causal relationships based purely on observed data [15]. The experi-\nmental results found that ChatGPT has some limitations in understanding new\n7","creator":"LaTeX with hyperref","page":6,"author":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"producer":"pdfTeX-1.40.21","keywords":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 30d0b0b5-c980-4fe1-a328-924f78b12b05
encoding: FLOAT32
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{"page_label":"8","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","subject":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","chroma:document":"knowledge and concepts beyond the existing textual training data corpus, that\nis, it only understands language commonly used to describe situations and not\nunderlying knowledge. In addition, its performance consistency and stability are\nnot high, as the experiment observed that it would provide diﬀerent answers for\nthe same question under multiple inquiries. However, despite the many limita-\ntions of ChatGPT, we believe that it has a great opportunity to improve causal\nrelationship research.\nIn the other ﬁeld\nGuo et al. [16] attempted to apply ChatGPT in the ﬁeld of communication,\nspeciﬁcally using ChatGPT for ordered importance semantic communication,\nwhere ChatGPT plays the role of an intelligent consulting assistant that can\nreplace humans in identifying the semantic importance of words in messages and\ncan be directly embedded into the current communication system. For a message\nto be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic","title":"","page":7,"keywords":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f766b82a-580b-464d-b7e0-c413da45bdc4
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{"page_label":"8","page":7,"producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","author":"","chroma:document":"to be transmitted, the sender ﬁrst utilizes ChatGPT to output the semantic\nimportance order of each word. Then, the transmitter executes an unequal error\nprotection transmission strategy based on the importance order to make the\ntransmission of important words in the message more reliable. The experimental\nresults show that the error rate and semantic loss of important words measured\nin the communication system embedded with ChatGPT are much lower than\nthose of existing communication schemes, indicating that ChatGPT can protect\nimportant words well and make semantic communication more reliable.\nWang et al. [17] studied the eﬀectiveness of ChatGPT in generating high-\nquality Boolean queries for systematic literature search. They designed a wide\nrange of prompts and investigated these tasks on more than 100 systematic\nreview topics. In the end, queries generated by ChatGPT achieved higher accu-\nracy compared to the currently most advanced query generation methods but","total_pages":35,"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 10550914-73c3-41b3-8179-0bf0d28c0a3c
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"8","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","chroma:document":"racy compared to the currently most advanced query generation methods but\nat the cost of reduced recall. For time-limited rapid reviews, it is often accept-\nable to trade oﬀ higher precision for lower recall. Additionally, ChatGPT can\ngenerate high search accuracy Boolean queries by guiding the prompts. How-\never, it should be noted that when two queries use the same prompts, ChatGPT\ngenerates diﬀerent queries, indicating its limitations in consistency and stabil-\nity. Overall, this study demonstrated the potential of ChatGPT in generating\neﬀective Boolean queries for systematic literature searches.\n2.1.2 Text Classiﬁcation\nThe purpose of text classiﬁcation is to assign text data to predeﬁned categories.\nThis task is critical for many applications, including sentiment analysis, spam\ndetection, and topic modeling. While traditional machine learning algorithms\nhave been widely used for text classiﬁcation, recent advances in natural lan-","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","author":"","title":"","page":7,"total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1cb03dbe-1e79-44fc-9d21-6108f2084dd6
encoding: FLOAT32
metadata:
{"keywords":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"have been widely used for text classiﬁcation, recent advances in natural lan-\nguage processing have led to the development of more advanced techniques.\nChatGPT has shown immense potential in this ﬁeld. Its ability to accurately\nclassify text, ﬂexibility in handling various classiﬁcation tasks, and potential for\ncustomization make it a valuable tool for text classiﬁcation, as evidenced by\nseveral studies in the literature.\n8","moddate":"2023-04-05T00:33:07+00:00","author":"","trapped":"/False","total_pages":35,"page":7,"producer":"pdfTeX-1.40.21","page_label":"8","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 15ff4e33-8794-4580-bfd9-88a4133c74a3
encoding: FLOAT32
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{"page_label":"9","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21","chroma:document":"Kuzman et al. [18] employed ChatGPT for automatic genre recognition, with\nthe goal of simplifying the text classiﬁcation task by utilizing ChatGPT’s zero-\nshot classiﬁcation capability. They compared ChatGPT’s genre recognition per-\nformance, using two prompt languages (EN and SL), with the X-GENRE clas-\nsiﬁer based on the multilingual model XLM-RoBERTa on the English dataset\nEN-GINCO and the Slovenian dataset GINCO. The results showed that when\nEN was used as the prompt language, ChatGPT achieved Micro F1, Macro F1,\nand Accuracy scores of 0.74, 0.66, and 0.72. However, on the GINCO dataset,\nChatGPT’s genre recognition performance with both EN and SL prompt lan-\nguages was lower than that of the X-GENRE classiﬁer to varying degrees.\nAmin et al. [19] evaluated the text classiﬁcation ability of ChatGPT in\naﬀective computing by using it to perform personality prediction, sentiment\nanalysis, and suicide ideation detection tasks. They prompted ChatGPT with","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","page":8,"total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c1e0ea5a-ce34-4b89-b0d3-6c4c2d83eb40
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{"chroma:document":"analysis, and suicide ideation detection tasks. They prompted ChatGPT with\ncorresponding prompts on three datasets: First Impressions, Sentiment140, and\nSuicide and Depression, and compared its classiﬁcation performance with three\nbaseline models: RoBERTa-base, Word2Vec, and BoW. The results showed\nthat ChatGPT’s accuracy and UAR for the ﬁve personality classiﬁcations on\nthe First Impressions dataset were lower than the baseline methods to varying\ndegrees. On the Sentiment140 dataset, ChatGPT’s accuracy and UAR were 85.5\nand 85.5, respectively, which were better than the three baseline methods. On\nthe Suicide and Depression dataset, ChatGPT’s accuracy and UAR were 92.7\nand 91.2, respectively, which were lower than RoBERTa, the best-performing\nbaseline method.\nZhang et al. [20] employed ChatGPT for stance detection, which includes\nsupport and opposition. They used ChatGPT to classify the political stance\nof tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains","title":"","subject":"","author":"","producer":"pdfTeX-1.40.21","page_label":"9","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","creator":"LaTeX with hyperref","page":8,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0e8a6588-5b2e-4193-ac6c-1e4c5367ee8c
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{"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"of tweets in the SemEval-2016 and P-Stance datasets. SemEval-2016 contains\n4,870 English tweets, and they selected tweets with the most commonly occur-\nring FM, LA, and HC political labels for stance classiﬁcation. The P-Stance\ndataset has 21,574 English tweets, and they classiﬁed the stance of tweets to-\nwards Trump, Biden, and Bernie. The ﬁnal results showed that on the SemEval-\n2016 dataset, ChatGPT achieved F1-m scores of 68.4, 58.2, and 79.5 for the FM,\nLA, and HC political labels, and F1-avg scores of 72.6, 59.3, and 78.0, respec-\ntively. On the P-Stance dataset, ChatGPT achieved F1-m scores of 82.8, 82.3,\nand 79.4 for the Trump, Biden, and Bernie political ﬁgures, and F1-avg scores\nof 83.2, 82.0, and 79.4, respectively.\nHuang et al. [21] used ChatGPT to detect implicit hate speech in tweets.\nThey selected 12.5% (795 tweets) of the LatentHatred dataset containing im-\nplicit hate speech and asked ChatGPT to classify them into three categories:","total_pages":35,"page_label":"9","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","author":"","creator":"LaTeX with hyperref","page":8,"title":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 06247434-4092-4113-8c47-2f5eb1dfd00b
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","title":"","page":8,"total_pages":35,"subject":"","author":"","chroma:document":"plicit hate speech and asked ChatGPT to classify them into three categories:\nimplicit hate speech, non-hate speech, and uncertain. The results showed that\nChatGPT correctly recognized 636 (80%) of the tweets. The number of tweets\nclassiﬁed as non-hate speech and uncertain were 146 (18.4%) and 13 (1.6%),\nrespectively. The results of the reclassiﬁcation of tweets in the non-hate speech\nand uncertain categories by Amazon Mechanical Turk (Mturk) workers were\nconsistent with ChatGPT’s classiﬁcation.\nOverall, ChatGPT has tremendous potential in text classiﬁcation tasks, as it\ncan eﬀectively address problems such as genre identiﬁcation, sentiment analysis,\n9","keywords":"","trapped":"/False","page_label":"9","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f887bbe1-ba05-4929-87e3-ef2489c10b59
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","page":9,"chroma:document":"stance detection, and more. However, there are still challenges that ChatGPT\nfaces in the ﬁeld of text classiﬁcation. Firstly, it struggles to perform well in\nclassiﬁcation tasks with rare or out-of-vocabulary words since it heavily relies\non the distribution of training data. Additionally, the signiﬁcant computational\nresources required for training and utilizing ChatGPT can limit its use in some\napplications.\n2.1.3 Text generation\nWe live in an era of information explosion, and text is an eﬃcient way of trans-\nmitting information. The diversity of information has led to a diversity of text\ncategories. When researchers use ChatGPT’s text generation capabilities for\nresearch, they inevitably choose to generate diﬀerent types of text. In the pro-\ncess of reading papers, we found that the word count of the text generated by\nresearchers increased from small to large, so we wanted to summarize existing\nresearch based on the size of the text word count. We divided the generated","subject":"","page_label":"10","producer":"pdfTeX-1.40.21","title":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 311e288a-ab4c-417b-a158-29b0c6d2c733
encoding: FLOAT32
metadata:
{"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page":9,"chroma:document":"research based on the size of the text word count. We divided the generated\ntext into three levels: phrases, sentences, and paragraphs.\nThe following article uses ChatGPT to generate phrases. Zhang et al. [22]\nproves that the semantic HAR model with semantic augmentation added dur-\ning training performs better in motion recognition than other models. Semantic\naugmentation requires shared tokens, which is lacking in some datasets. There-\nfore, authors leverage ChatGPT for an automated label generation approach\nfor datasets originally without shared tokens. Fu et al. [23] describes a new\nworkﬂow for converting natural language commands into Bash commands. The\nauthor uses ChatGPT to generate a candidate list of Bash commands based\non user input, and then uses a combination of heuristic and machine learning\ntechniques to rank and select the most likely candidates. This workﬂow was\nevaluated on a real command dataset and achieved high accuracy compared","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","page_label":"10","author":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ea01c99f-7493-4e64-b59e-38fea78b5396
encoding: FLOAT32
metadata:
{"page":9,"total_pages":35,"title":"","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","author":"","chroma:document":"evaluated on a real command dataset and achieved high accuracy compared\nto other state-of-the-art methods. Chen et al. [24] used the Bart model and\nChatGPT for the task of summarizing humorous titles and compared the perfor-\nmance of the two models. It was found that the Bart model performed better on\nlarge datasets, but ChatGPT was competitive with our best ﬁne-tuned model\nin a small range (48), albeit slightly weaker.\nThe following article uses ChatGPT to generate sentences.Chen et al. [25]\nconstructed a dialogue dataset (HPD) with scenes, timelines, character at-\ntributes, and character relationships in order to use ChatGPT as a conver-\nsational agent to generate dialogue. However, ChatGPT’s performance on the\ntest set was poor, and there is room for improvement.In study [26], chatGPT\ndemonstrated its ability to simplify complex text by providing three ﬁctional\nradiology reports to chatGPT for simpliﬁcation. Most radiologists found the","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","page_label":"10","trapped":"/False"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f6a438b9-7bca-4800-88d1-39954433c1d0
encoding: FLOAT32
metadata:
{"page":9,"total_pages":35,"chroma:document":"radiology reports to chatGPT for simpliﬁcation. Most radiologists found the\nsimpliﬁed reports to be accurate and complete, with no potential harm to pa-\ntients. However, some errors, omissions of critical medical information and text\npassages were identiﬁed, which could potentially lead to harmful conclusions if\nnot understood by the physicians. Xia et al. [27] proposes a new program re-\npair paradigm called Session-based Automatic Program Repair (APR). In APR,\nthe previously generated patches are iteratively built upon by combining them\n10","keywords":"","trapped":"/False","creator":"LaTeX with hyperref","title":"","producer":"pdfTeX-1.40.21","page_label":"10","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","author":"","creationdate":"2023-04-05T00:33:07+00:00","subject":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 13276108-4787-49ea-bfb9-a0200eea3f8c
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":10,"chroma:document":"with validation feedback to construct the model’s input. The eﬀectiveness of\nthe approach is veriﬁed using the QuixBugs dataset. The experiment shows\nthat ChatGPT ﬁne-tuned with reinforcement learning from human feedback\n(RLHF) outperforms Codex trained unsupervisedly in both repair datasets. In\nreference to study [28], ChatGPT was compared to three commercial transla-\ntion products: Google Translate2, DeepL Translate3, and Tencent TranSmart4.\nThe evaluation was conducted on the Flores101 test set, using the WMT19\nbiomedical translation task to test translation robustness, with BLEU score as\nthe main metric. The study found that ChatGPT is competitive with commer-\ncial translation products on high-resource European languages but falls behind\non low-resource or distant languages. The authors explored an interesting strat-\negy called pivot prompts, which signiﬁcantly improved translation performance.\nWhile ChatGPT did not perform as well as commercial systems on biomedical","moddate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"page_label":"11","trapped":"/False","subject":"","title":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","keywords":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"title":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"chroma:document":"While ChatGPT did not perform as well as commercial systems on biomedical\nabstracts or Reddit comments, it may be a good speech translator. Prieto et\nal. [29] evaluated the use of ChatGPT in developing an automated construction\nschedule based on natural language prompts. The experiment required building\nnew partitions in an existing space and providing details on the rooms to be\npartitioned. The results showed that ChatGPT was able to generate a coher-\nent schedule that followed a logical approach to meet the requirements of the\ngiven scope. However, there were still several major ﬂaws that would limit the\nuse of this tool in real-world projects.Michail et al. [30] proposed a method\nto improve the prediction accuracy of the HeFit ﬁne-tuned XLM T model on\ntweet intimacy by generating a dataset of tweets with intimacy rating tags using\nChatGPT. The speciﬁc operation is to input tweets with intimacy rating tags\ninto ChatGPT and then output similar tweets.","page":10,"keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","page_label":"11","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 050f465d-1826-417b-9c91-33d283aa0668
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","title":"","author":"","chroma:document":"into ChatGPT and then output similar tweets.\nThe following article uses ChatGPT to generate paragraphs. Wang et al.\n[31] compared the abstract summarization performance of ChatGPT and other\nmodels on various cross-lingual text datasets and found that ChatGPT may\nperform worse in metrics such as R 1, R 2, R L, and B S. Yang et al. [32]\nsummarized the performance of ChatGPT in question answering-based text\nsummarization and found that, compared to ﬁne-tuned models, ChatGPT’s\nperformance is slightly worse in all performance metrics. However, the article\nsuggests that if the dataset is golden annotation, ChatGPT’s performance may\nsurpass ﬁne-tuned models in these metrics. Belouadi et al. [33] compared the\nability of ByGPT5 and ChatGPT trained on a range of labeled and unlabeled\ndatasets of English and German poetry to generate constrained style poetry, and\nevaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-","total_pages":35,"page":10,"subject":"","moddate":"2023-04-05T00:33:07+00:00","page_label":"11","keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3ed47e3f-c354-4781-9542-3db6aa96df55
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{"subject":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":10,"creator":"LaTeX with hyperref","trapped":"/False","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","keywords":"","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"evaluated them using three metrics: Rhyme, ScoreAlliteration, and ScoreMe-\nter Score. The conclusion is that ByGPT5 performs better than ChatGPT.\nBlanco-Gonzalez et al. [34] evaluated chatGPT’s ability to write commentary\narticles, and in fact, this article itself was written by chatGPT. The human au-\nthor rewrote the manuscript based on chatGPT’s draft. Experts found that it\ncan quickly generate and optimize text, as well as help users complete multiple\ntasks. However, in terms of generating new content, it is not ideal. Ultimately,\nit can be said that without strong human intervention, chatGPT is not a use-\nful tool for writing reliable scientiﬁc texts. It lacks the knowledge and expertise\nrequired to accurately and fully convey complex scientiﬁc concepts and informa-\n11","page_label":"11"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: cf6b7006-83a6-4a1f-8979-b2ea398af706
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{"page":11,"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":"","subject":"","keywords":"","author":"","chroma:document":"tion. Khalil et al. [35] on the originality of content generated by ChatGPT. To\nevaluate the originality of 50 papers on various topics generated by ChatGPT,\ntwo popular plagiarism detection tools, Turnitin and iThenticate, were used.\nThe results showed that ChatGPT has great potential in generating complex\ntext output that is not easily captured by plagiarism detection software. The\nexisting plagiarism detection software should update their plagiarism detection\nengines. Basic et al. [36] conducted a comparison of the writing performance\nof students using or not using ChatGPT-3 as a writing aid. The experiment\nconsisted of two groups of 9 participants each. The control group wrote articles\nusing traditional methods, while the experimental group used ChatGPT as an\naid. Two teachers evaluated the papers. The study showed that the assistance of\nChatGPT did not necessarily improve the quality of the students’ essays.Noever","page_label":"12"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6e744bf9-b3fc-46b9-92cb-f0cbafc53d28
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{"keywords":"","producer":"pdfTeX-1.40.21","chroma:document":"ChatGPT did not necessarily improve the quality of the students’ essays.Noever\net al. [37] discusses the potential of using artiﬁcial intelligence (AI), particularly\nlanguage models like GPT (including GPT-3), to create more convincing chat-\nbots that can deceive humans into thinking they are interacting with another\nperson. The article describes a series of experiments in which they used GPT-3\nto generate chatbot responses that mimic human-like conversations and were\ntested on human participants. The results show that some participants were\nunable to distinguish between the chatbot and a real human, highlighting the\npotential for these AI chatbots to be used for deceptive purposes.\n2.1.4 Code Generation\nCode generation refers to the process of automatically generating computer code\nfrom high-level descriptions or speciﬁcations. ChatGPT’s advanced natural lan-\nguage processing capabilities make it capable of performing code generation","total_pages":35,"subject":"","title":"","trapped":"/False","page":11,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","page_label":"12","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a80427a0-6cc1-4411-987a-fb0989a82da1
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{"author":"","trapped":"/False","chroma:document":"guage processing capabilities make it capable of performing code generation\ntasks. By analyzing the requirements for code generation, ChatGPT can pro-\nduce code snippets that accurately execute the intended functionality. This not\nonly saves time and eﬀort in writing code from scratch but also reduces the risk\nof errors that may occur during manual coding. In addition, ChatGPT’s ability\nto learn and adapt to new programming languages and frameworks enables it\nto complete more complex programming tasks. For example:\nMegahed et al. [38] discussed the potential of using ChatGPT for tasks\nsuch as code explanation, suggesting alternative methods for problem-solving\nwith code, and translating code between programming languages. The solutions\nprovided by ChatGPT were found to be viable. In another study, Treude et\nal. [39] introduced a ChatGPT-based prototype called GPTCOMCARE, which\nhelps programmers generate multiple solutions for a programming problem and","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":11,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","total_pages":35,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"12","subject":"","moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4b2272a6-bb75-43be-abe4-ffc17827a22d
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{"total_pages":35,"creator":"LaTeX with hyperref","chroma:document":"helps programmers generate multiple solutions for a programming problem and\nhighlight the diﬀerences between each solution using colors.Sobania et al. [40]\nutilized ChatGPT for code bug ﬁxing, and further improved the success rate of\nbug ﬁxing by inputting more information through its dialogue system. Speciﬁ-\ncally, the QuixBugs standard bug ﬁxing benchmark contained 40 code bugs that\nneeded to be ﬁxed. With limited information, ChatGPT ﬁxed 19 bugs, which\nwas slightly lower than the 21 bugs ﬁxed by the Codex model, but signiﬁcantly\nhigher than the 7 ﬁxed by the Standard APR model. When given more prompts\nand information, ChatGPT was able to ﬁx 31 bugs, demonstrating its potential\n12","page_label":"12","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","keywords":"","page":11,"moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 22a714f3-d9dd-48fd-9892-e64d6c114cb1
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{"total_pages":35,"subject":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","page_label":"13","chroma:document":"for code bug ﬁxing tasks.Xia et al. [27] proposed a conversational approach\nfor Automated Program Repair (APR), which alternates between generating\npatches and validating them against feedback from test cases until the correct\npatch is generated. Selecting 30 bugs from the QuixBugs standard bug ﬁxing\nbenchmark, which are suitable for test case feedback, and demonstrating them\nwith Java and Python, the QuixBugs-Python and QuixBugs-Java datasets were\nobtained. The conversational APR using ChatGPT outperformed the conversa-\ntional APR using Codex and the conversational APR using CODEGEN (with\nmodel parameters of 350M, 2B, 6B, and 16B) on both datasets. Furthermore,\nChatGPT’s conversational APR generated and validated patches with signiﬁ-\ncantly fewer feedback loops than the other models.\nChatGPT can not only be used to achieve some simple code generation tasks\nbut also can be used to accomplish some complex programming tasks. Noever","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","author":"","keywords":"","page":12,"trapped":"/False"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0b5a8663-b23c-40ad-8ca9-e42880903925
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{"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"but also can be used to accomplish some complex programming tasks. Noever\net al. [41] tested ChatGPT’s code generation capabilities on four datasets -\nIris, Titanic, Boston Housing, and Faker. When prompted to mimic a Python\ninterpreter in the form of a Jupyter notebook, the model was able to generate\nindependent code based on the prompt and respond with the expected out-\nput. For example, when given the prompt ”data.cor()” for the Iris dataset,\nChatGPT generated correct Python output. The test results indicate that\nChatGPT can access structured datasets and perform basic software opera-\ntions required by databases, such as create, read, update, and delete (CRUD).\nThis suggests that cutting-edge language models like ChatGPT have the nec-\nessary scale to tackle complex problems. McKee et al. [42] utilized ChatGPT\nas an experimental platform to investigate cybersecurity issues. They modeled\nﬁve diﬀerent modes of computer virus properties, including self-replication, self-","trapped":"/False","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page":12,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"13"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fa93f724-5175-4816-90fb-ba2f78f66d28
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{"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","keywords":"","trapped":"/False","page_label":"13","total_pages":35,"page":12,"title":"","subject":"","chroma:document":"ﬁve diﬀerent modes of computer virus properties, including self-replication, self-\nmodiﬁcation, execution, evasion, and application, using ChatGPT. These ﬁve\nmodes encompassed thirteen encoding tasks from credential access to defense\nevasion within the MITRE ATT&CK framework. The results showed that the\nquality of ChatGPT’s generated code was generally above average, except for the\nself-replication mode, where it performed poorly.They [43] also employed Chat-\nGPT as a network honeypot to defend against attackers. By having ChatGPT\nmimic Linux, Mac, and Windows terminal commands and providing interfaces\nfor TeamViewer, nmap, and ping, a dynamic environment can be created to\nadapt to attackers’ operations, and logs can be used to gain insight into their\nattack methods, tactics, and procedures. The authors demonstrated ten hon-\neypot tasks to illustrate that ChatGPT’s interface not only provides suﬃcient\nAPI memory to execute previous commands without defaulting to repetitive","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9c9241ef-5404-4467-850b-925eed531950
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{"page_label":"13","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","author":"","keywords":"","chroma:document":"API memory to execute previous commands without defaulting to repetitive\nintroductory tasks but also oﬀers a responsive welcome program that maintains\nattackers’ interest in multiple queries.\nIn the ﬁeld of code generation, there are still several challenges with Chat-\nGPT. Firstly, its application scope is limited as its training data is biased to-\nwards programming languages such as Python, C++, and Java, making it po-\ntentially unsuitable for some programming languages or coding styles. Secondly,\nmanual optimization is necessary for code formatting, as the generated code may\nnot be performance-optimized or follow best coding practices, requiring manual\nediting and optimization. Lastly, the quality of the generated code cannot be\n13","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","subject":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":12}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 73909120-5595-49c6-91e9-60c4447c1914
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{"keywords":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","title":"","subject":"","page_label":"14","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"author":"","chroma:document":"guaranteed, as it heavily relies on the quality of the natural language input,\nwhich may contain errors, ambiguities, or inconsistencies, ultimately aﬀecting\nthe accuracy and reliability of the generated code.\n2.1.5 Inference\nInference refers to the process of drawing new conclusions or information through\nlogical deduction from known facts or information. It is typically based on a se-\nries of premises or assumptions, and involves applying logical rules or reasoning\nmethods to arrive at a conclusion. Inference is an important ability in human\nthinking, and is often used to solve problems, make decisions, analyze and eval-\nuate information, etc. Inference also plays a key role in ﬁelds such as science,\nphilosophy, law, etc. There are two types of inference: inductive reasoning,\nwhich involves deriving general rules or conclusions from known facts or expe-\nriences, and deductive reasoning, which involves deriving speciﬁc conclusions","page":13,"producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0c0b2982-a01e-4e88-af92-1dfc92c4268e
encoding: FLOAT32
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{"title":"","author":"","page_label":"14","producer":"pdfTeX-1.40.21","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","chroma:document":"riences, and deductive reasoning, which involves deriving speciﬁc conclusions\nfrom known premises or assumptions. Whether inductive or deductive, the pro-\ncess of inference requires following strict logical rules to ensure the correctness\nand reliability of the inference.\nSome papers attempt to use ChatGPT’s ability in inductive reasoning to\ncapture the meaning in text and use deﬁned metrics to score the text. Michail\net al. [30] uses ChatGPT to infer intimacy expressed in tweets. They ﬁrst input\n50 tweets with intimacy markers to ChatGPT, then use inductive reasoning to\ninfer the standards for generating tweets with diﬀerent levels of intimacy, and\nﬁnally generate ten tweets with intimacy values ranging from 0 to 5. Susnjak\net al. [44] collected a large amount of textual data from patient-doctor dis-\ncussion forums, patient testimonials, social media platforms, medical journals,\nand other scientiﬁc research publications. Using the BERT model, the author","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":13,"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6580ffc3-c292-4db3-ac50-32cd77a6b6db
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{"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"and other scientiﬁc research publications. Using the BERT model, the author\ninferred emotion values from 0 to 1. The author visualized the process of how\nthe presence of bias in the discourse surrounding chronic manifestations of the\ndisease using the SHAP tool. The author also envisioned ChatGPT as a replace-\nment for the BERT model for scoring the emotional value of text. Huang et al.\n[21] chose 12.5% of individuals in the potential hate dataset as study materials,\ninduced ChatGPT to make classiﬁcations based on a prompt, and ChatGPT\nproduced three classiﬁcations: unclear, yes, and no. The author assigned a\nvalue of 1 to yes, -1 to no, and 0 to unclear, and had ChatGPT score and clas-\nsify them. ChatGPT was able to correctly classify 80% of implicit hate tweets\nin the author’s experimental setup, demonstrating ChatGPT’s great potential\nas a data labeling tool using simple prompts.\nSome papers have evaluated ChatGPT’s reasoning performance, mainly in","total_pages":35,"page_label":"14","trapped":"/False","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00","page":13}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f6419601-97d4-42c8-95be-a28d17188e30
encoding: FLOAT32
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{"trapped":"/False","page":13,"total_pages":35,"title":"","author":"","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"14","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"Some papers have evaluated ChatGPT’s reasoning performance, mainly in\ndecision-making and spatial reasoning, and identifying ambiguity. Tang et al.\n[45] used the independence axiom and the transitivity axiom, as well as other\nnon-VNM related decision-making abilities, by presenting bets conditioned on\nrandom events, bets with asymmetric outcomes, decisions encapsulating Sav-\nage’s Sure Thing principle, and other complex bet structures like nested bets, to\ndesign experiments where each experiment input a short prompt to ChatGPT\nand evaluated the results. The conclusion is that ChatGPT exhibits uncer-\n14"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c4bc7381-4ae7-4d6b-8e4f-2f6daca6e991
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{"total_pages":35,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","page_label":"15","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","author":"","title":"","subject":"","trapped":"/False","chroma:document":"tainty in the decision-making process: in some cases, large language models\ncan arrive at the correct answer through incorrect reasoning; and it may make\nsuboptimal decisions for simple reasoning problems. Ortega-Martn et al. [46]\nhad ChatGPT detect three diﬀerent levels of language ambiguity and evaluated\nits performance. The conclusion is that In semantics, ChatGPT performed per-\nfectly in the detection of ambiguities. Apart from that, it has some bright sports\n(co-reference resolution) and some weaknesses (puts gender bias over grammar\nin some non-ambiguous situations). In the generation task ChatGPT did well,\nbut also revealed some of its worse issues: the lack of systematicity. Lastly, it\nshould also be pointed that in most of the cases ChatGPT brilliantly alludes to\nlack of context as the key factor in disambiguation.\n2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":14,"creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a9fdc0e5-5ea7-4454-a8c1-345a8df014fd
encoding: FLOAT32
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{"author":"","trapped":"/False","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":14,"producer":"pdfTeX-1.40.21","total_pages":35,"title":"","chroma:document":"2.1.6 Data or information extraction, transformation, enhancement,\nprocessing\nData Visualization\nNatural language interfaces have contributed to generating visualizations\ndirectly from natural language, but visualization problems remain challenging\ndue to the ambiguity of natural language.ChatGPT provides a new avenue for\nthe ﬁeld by converting natural language into visualized code.\nIn terms of data visualization, Noever et al. [41] tested ChatGPT’s ba-\nsic arithmetic skills by asking questions.On the iris dataset, Titanic survival\ndataset, Boston housing data, and randomly generated insurance claims dataset,\nthe statistical analysis of data and visualization problems were converted to\nprogramming problems using Jupyter to verify ChatGPT’s ability to generate\npython code to draw suitable graphs and analyze the data. The results show\nthat ChatGPT can access structured and organized datasets to perform the\nfour basic software operations required for databases: create, read, update, and","subject":"","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","page_label":"15"}


created_at: 2026-05-29 09:24:38
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{"keywords":"","subject":"","author":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","page":14,"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","page_label":"15","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"four basic software operations required for databases: create, read, update, and\ndelete, and generate suitable python code to plot graphs for descriptive statis-\ntics, variable correlation analysis, describing trends, and other data analysis\noperations.Maddigan et al. [47] proposed an end-to-end solution for visualizing\ndata in natural language using LLM, which uses an open-source python frame-\nwork designed to generate appropriate hints for selected datasets to make LLM\nmore eﬀective in understanding natural language, and uses internal reasoning\ncapabilities to select the appropriate visualization type to generate the code for\nvisualization. In this paper,the reseachers compare the visualization results of\nGPT-3, Codex and ChatGPT in the case of nvBench SQLite database [48] and\nthe visualization results of energy production dataset in the study of ADVISor\nwith NL4DV [49, 50].In addition to, they explore the ability to reason and hy-"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9eeba8ba-b3a9-4d84-b6c8-e999a761f691
encoding: FLOAT32
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{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"author":"","page":14,"creator":"LaTeX with hyperref","keywords":"","subject":"","chroma:document":"with NL4DV [49, 50].In addition to, they explore the ability to reason and hy-\npothesize of the LLM on movie dataset [48] when the hints are insuﬃcient or\nwrong .Experimental results show that LLM can eﬀectively support the end-to-\nend generation of visualization results from natural language when supported\nby hints, providing an eﬃcient, reliable and accurate solution to the natural\nlanguage visualization problem.\n15","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"15"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 22da0159-e808-4d75-85a9-3c85b47d7d0e
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"Information Extraction\nThe goal of information extraction is to extract speciﬁc information from\nnatural language text for structured representation, including three important\nsubtasks such as entity relationship extraction, named entity recognition, and\nevent extraction, which have wide applications in business, medical, and other\nﬁelds.\nIn information extraction, Wei et al. [51] proposed ChatIE, a ChatGPT-\nbased multi-round question-and-answer framework for information extraction.\nThe framework decomposes a complex information extraction (IE) task into\nseveral parts, then combines the results of each round into a ﬁnal structured\nresult. The entity association triple extraction, named entity recognition, and\nevent extraction tasks were performed on six datasets NYT11-HRL, DuIE2.0\n, conllpp, MSR , DuEE1.0 [52, 53, 54, 55, 56], and ACE05 in both languages,\ncomparing three metrics of precision, recall, and F1 score.These results sug-","keywords":"","creator":"LaTeX with hyperref","trapped":"/False","total_pages":35,"page":15,"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"16","title":"","subject":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 427c43c9-4ed4-47cf-a28d-7578d10a87d8
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","page":15,"creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"comparing three metrics of precision, recall, and F1 score.These results sug-\ngest that on six widely used IE datasets, ChatIE improves performance by an\naverage of 18.98% compared to the original ChatGPT without ChatIE, and out-\nperforms the supervised models FCM and MultiR [57, 58] on the NYT11-HRL\ndataset.While the original ChatGPT cannot solve complex IE problems with\noriginal task instructions, and with this framework, successfully IE tasks were\nimplemented on six datasets.Gao et al. [59] explored the feasibility and chal-\nlenges of ChatGPT for event extraction on the ACE2005 corpus, evaluating the\nperformance of ChatGPT in long-tail and complex scenarios (texts containing\nmultiple events) and comparing it with two task-speciﬁc models, Text2Event\nand EEQA [60, 61].Then,they explored the impact of diﬀerent cues on perfor-\nmance of ChatGPT. The results show that the average performance of Chat-\nGPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc","moddate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"16","creator":"LaTeX with hyperref","subject":"","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 491a098a-f2bc-4659-92f9-b97389718bae
encoding: FLOAT32
metadata:
{"page_label":"16","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"GPT in long-tail and complex scenarios is only 51.04% of that of task-speciﬁc\nmodels such as EEQA. Continuous reﬁnement of cues does not lead to consis-\ntent performance improvements, and ChatGPT is highly sensitive to diﬀerent\ncue styles.Tang et al. [62] proposed a new training paradigm that incorporates\nappropriate cues to guide ChatGPT to generate a variety of examples with dif-\nferent sentence structures and language patterns and eliminate the resulting\nlow-quality or duplicate samples for downstream tasks. Although compared to\na soft model for a speciﬁc healthcare tasks, ChatGPT underperforms in Named\nEntity Recognition (NER) and Relationship Extraction (RE) tasks , in the Gene\nAssociation Database (GAD) Release; EU-ADR corpus for the RE task , the\ninnovative training framework was able to train local models, with F1 scores im-\nproving from 23.37% to 63.99% for the named entity recognition task and from","title":"","page":15,"total_pages":35,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 51cfe529-7098-4e07-ab5b-3e9ba139c8aa
encoding: FLOAT32
metadata:
{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":15,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page_label":"16","trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","author":"","chroma:document":"proving from 23.37% to 63.99% for the named entity recognition task and from\n75%, while alleviating privacy concerns and time-consuming data collection and\nannotation problems.He et al. [63] proposed a contextual learning framework\nICL- D3IE. this framework introduces formatted presentation, continuously it-\nerates to update and improve the presentation, and then combines ChatGPT\nfor text information extraction. In the paper, ICL-D3IE is compared with ex-\nisting pre-trained models such as LiLT,BROS (in-distribution (ID) setting and\nout-of-distribution (OOD) setting) on datasets (FUNSD, CORD, and SROIE\n[64, 65, 66]).These results show that the ICL-D3IE method in all datasets and\nsettings except for the ID setting on CORD are superior to other methods,\n16","subject":"","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fe2c7d2c-bb9d-476c-9c9b-6e85ab108073
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page_label":"17","creator":"LaTeX with hyperref","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","producer":"pdfTeX-1.40.21","chroma:document":"with ICL-D3IE (GPT-3) F1 scores reaching 90.32% on FUNSD and97.88% on\nSROIE; in the out-of-distribution (OOD) setting, ICL-D3IE performs much\nbetter than previous pre-trained methods on all datasets.Polak et al. [67] pro-\nposed ChatExtract method - consisting of a set of engineering prompts applied\nto a conversational LLM - for automatic data extraction. In the experiment,\nthey extracted a large number of sentences from hundreds of papers and ran-\ndomly selected 100 sentences containing data and 100 sentences without data\nas test data. The results show that the accuracy and recall of LLM exceeded\n90% and may be comparable to human accuracy in many cases; in addition to\nthis, the experiments were conducted under the condition of removing follow-up\nprompts and not keeping the conversation compared to previous experiments,\nrespectively. The accuracy of deleting follow-up questions dropped to 80.2%\nand the recall rate dropped to 88.0%. Removing the conversational aspect and","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":16}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: bbd7e7f1-9c5d-4569-9af5-577aa3d83b79
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"and the recall rate dropped to 88.0%. Removing the conversational aspect and\nrelated information retention recall and accuracy dropped to 90.0% and 56.6%,\nrespectively, demonstrating the eﬀect of information retention combined with\npurposeful redundancy on LLM information extraction performance.\nQuality Assessment\nFor translation quality, text generation quality, manual assessment is usually\neﬀective but suﬀers from subjectivity and time-consuming, etc. It was found\nthrough exploration that ChatGPT has also achieved signiﬁcant performance\nin automatic quality assessment.\nIn terms of quality assessment,Kocmi et al. [68] proposed a GPT-based\ntranslation quality assessment metric, GEMBA, which evaluates the transla-\ntion of each fragment individually and then averages all the obtained scores to\nobtain a ﬁnal system-level score. In the MQM2022 test set (English-German,\nEnglish-Russian, and Chinese-English) [69], a scoring task was performed with","keywords":"","page":16,"title":"","subject":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"trapped":"/False","page_label":"17","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4341e4e3-9399-45d5-af34-997051d9c956
encoding: FLOAT32
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{"creator":"LaTeX with hyperref","subject":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","title":"","page_label":"17","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":16,"chroma:document":"English-Russian, and Chinese-English) [69], a scoring task was performed with\na classiﬁcation task to compare the accuracy [70] and kendall tau scores [71] of\nseven GPT models under four cue templates.The results showed that GEMBA\nhad the highest system-level accuracy of 88.0% compared to more than 10 au-\ntomatic metrics such as BLEU, and among the seven GPT models, ChatGPT\naccuracy is above 80%, in addition to, the best performance can be obtained in\nthe least constrained template, demonstrating the potential of LLM for trans-\nlation quality assessment tasks, but the evaluation is only applicable at the\nsystem level and needs further improvement.Wang et al. [72] used ChatGPT\nas a natural language generation (NLG) evaluator to study the correlation with\nhuman judgment. On three datasets covering diﬀerent NLG tasks, task- and\naspect-speciﬁc cues were designed to guide ChatGPT for NLG evaluation in\nCNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: fdfd3342-0f58-457f-a193-1fbc7dc5e55d
encoding: FLOAT32
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{"trapped":"/False","subject":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","chroma:document":"CNN/DM [73], OpenMEVA-ROC, and BAGEL for summary, story generation,\nand data-to-text scoring, respectively. Then,they compute Spearman coeﬃ-\ncients [74],Pearson correlation coeﬃcients [75]. Kendall’s Tau score [76] to as-\nsess the correlation with human evaluations.The results show that ChatGPT is\nhighly correlated with human judgments in all aspects, with correlation coeﬃ-\ncients of 0.4 or more in all categories, showing its potential as an NLG indicator.\n17","title":"","moddate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"page":16,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page_label":"17"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c9018bbb-d888-44f9-bdac-408503842ceb
encoding: FLOAT32
metadata:
{"subject":"","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","page_label":"18","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"chroma:document":"Data Augmentation\nIn natural language processing, text data augmentation is an eﬀective mea-\nsure to alleviate the problem of low data quantity and low quality training data,\nand ChatGPT has shown great potential in this regard.\nIn terms of data augmentation, Dai et al. [77] proposed a ChatGPT-based\ntext data augmentation method that reformulates each sentence in the train-\ning sample into multiple conceptually similar but semantically diﬀerent samples\nfor classiﬁcation tasks downstream of the Bert model.On text transcriptions\nand PubMed 20k datasets containing more than 8 hours of audio data of com-\nmon medical symptom descriptions,experiments were conducted to compare co-\nsine similarity and TransRate metrics with multiple data enhancement methods\n[9].This paper shows that compared with existing data enhancement methods,\nthe proposed ChatAug method shows a double-digit improvement in sentence\nclassiﬁcation accuracy and generates more diverse augmented samples while","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","keywords":"","moddate":"2023-04-05T00:33:07+00:00","page":17,"producer":"pdfTeX-1.40.21","author":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 628c8c3c-1199-4e77-affe-3dcb90322e6f
encoding: FLOAT32
metadata:
{"trapped":"/False","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","page":17,"author":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"classiﬁcation accuracy and generates more diverse augmented samples while\nmaintaining its accuracy, but the original model is not ﬁne-tuned in the pa-\nper and suﬀers from a lack of domain knowledge, which may produce incorrect\naugmented data.\nMultimodal fusion\nChatGPT can currently only process natural language directly, but with a\ncross-modal encoder, it can combine natural language with cross-modal pro-\ncessing to provide solutions for intelligent transportation, healthcare, and other\nﬁelds.\nIn terms of multimodal data processing, Wu et al. [78] constructed a frame-\nwork that Visual ChatGPT integrates with diﬀerent Visual Foundation Models\n(VFMs) and then combines a series of hints to input visual information to Chat-\nGPT to solve visual problems.The paper shows examples of visual tasks such\nas removing or replacing certain objects from images, interconversion between\nimages and text, demonstrating the Visual ChatGPT has great potential and","moddate":"2023-04-05T00:33:07+00:00","page_label":"18"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4e4ebd8a-0839-4eac-9740-01cc72dcb5bf
encoding: FLOAT32
metadata:
{"author":"","moddate":"2023-04-05T00:33:07+00:00","title":"","subject":"","producer":"pdfTeX-1.40.21","page_label":"18","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","page":17,"creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"images and text, demonstrating the Visual ChatGPT has great potential and\ncapability for diﬀerent tasks.But there are issues during the task that require\na large number of hints to convert VFMs to language, invoke multiple VFMs\nto solve complex problems leading to limited real-time capability, and security\nand privacy issues. Zheng et al. [79] showed a text mining example of LLM\nfor extracting self-driving car crash data from California crash news, analyz-\ning a failure report example, and generating a crash report example based on\nkeywords; introduced a use case concept of a smartphone-based framework for\nautomatic LLM failure report generation, which absorbs multiple data sources\ncaptured by cell phone sensors and then transfers the data to a language space\nfor text mining, inference and generation, and further outputs the key informa-\ntion needed to form a comprehensive fault report, demonstrating the potential\nof LLM for a variety of transportation tasks.","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: c9f1e95f-2839-4e55-8494-9fd8322c4581
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page":17,"subject":"","page_label":"18","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","trapped":"/False","keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"of LLM for a variety of transportation tasks.\nNowadays, ChatGPT shows a wide range of applications in data visualiza-\ntion, information extraction, data enhancement, quality assessment, and multi-\nmodal data processing.But there are also issues on how to further utilize hints\nto eﬀectively interact with ChatGPT, lack of ability to process and analyze data\n18"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: cd7fd383-bd2b-4d6a-b176-f1f107d36e62
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":18,"author":"","creator":"LaTeX with hyperref","title":"","total_pages":35,"subject":"","page_label":"19","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","chroma:document":"from devices such as sensors, and data privacy and security.\nCueing Techniques\nCue engineering provides important support for eﬀective dialogue with large\nlanguage models.White et al. [80] proposed a framework for cueing models\napplicable to diﬀerent domains. This framework structures cues to interact\nwith LLMs by providing speciﬁc rules and guidelines. Also, this paper presents\na catalog of cueing patterns that have been applied to LLM interactions, as well\nas speciﬁc examples with and without cues. The advantages of the combinability\nof prompting patterns are demonstrated, allowing users to interact with LLM\nmore eﬀectively, but patterns for reusable solutions and new ways to use LLM\nneed to be continuously explored.\n2.1.7 Human-ChatGPT Collaboration\nCollaboration between humans and machines is a process where humans and\nmachines work together to achieve a common goal. In such collaboration, hu-\nmans provide domain expertise, creativity, and decision-making abilities, while","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 21f3d8e6-f417-4887-af65-d3a929bae31d
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","chroma:document":"mans provide domain expertise, creativity, and decision-making abilities, while\nmachines provide automation, scalability, and computing power. ChatGPT is\nan advanced natural language processing model that can understand and gen-\nerate human-like language, thereby reducing communication costs. Its ability\nto process and generate natural language makes it an ideal partner for human\ncollaboration. ChatGPT can oﬀer relevant suggestions, complete tasks based\non human input, and enhance human productivity and creativity. It can learn\nfrom human feedback and adapt to new tasks and domains, further improv-\ning its performance in human-machine collaboration. ChatGPT’s capability to\ncomprehend natural language and produce appropriate responses makes it a\nvaluable tool for various collaboration applications, as demonstrated by several\nstudies in the literature we have gathered.\nAhmad et al. [81] proposed a method for human-machine collaboration us-","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page":18,"author":"","title":"","page_label":"19","total_pages":35,"creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 535eb051-74f4-4404-a5a4-b6c6f68a6121
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","chroma:document":"Ahmad et al. [81] proposed a method for human-machine collaboration us-\ning ChatGPT to create software architecture. This method transforms software\nstories (created by software architects based on application scenarios) into feasi-\nble software architecture diagrams through continuous interaction between the\nsoftware architect and ChatGPT. During the evaluation stage, ChatGPT uses\nthe Software Architecture Analysis Method (SAAM) to evaluate each compo-\nnent in the software architecture and generate evaluation reports. This method\neﬃciently utilizes the knowledge and supervision of the architect with the ca-\npabilities of ChatGPT to collaboratively build software-intensive systems and\nservices. Lanzi et al. [82] proposed a collaborative design framework that com-\nbines interactive evolution and ChatGPT to simulate typical human design pro-\ncesses. Humans collaborate with large language models (such as ChatGPT) to","page":18,"subject":"","keywords":"","page_label":"19"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 205ede45-524a-4f5e-89fb-92627d664a71
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","keywords":"","page_label":"19","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"cesses. Humans collaborate with large language models (such as ChatGPT) to\nrecombine and transform ideas, and use genetic algorithms to iterate through\ncomplex creative tasks. The results of three game design tasks showed that the\nframework received positive feedback from game designers. The framework has\ngood reusability and can be applied to any design task that can be described in\nfree text form.\n19","page":18,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21","title":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6a4617b5-3949-4de0-978d-17555ab03761
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{"title":"","author":"","chroma:document":"In the future, ChatGPT’s ability to understand nonverbal cues such as tone\nof voice and body language can be enhanced, enabling it to better understand\nhuman thoughts and interact with people more eﬀectively.\n2.1.8 ChatGPT Integration\nIntegration refers to combining diﬀerent systems or software components to\nachieve a common goal. ChatGPT can be integrated as a part of a whole or\nact as an integration tool to enable seamless communication between diﬀerent\nsystems. Its natural language processing ability makes it easier for non-technical\nusers to interact with systems, reducing the need for specialized knowledge or\ntraining. Some studies in the literature we collected have already demonstrated\nthis.\nTreude et al. [39] integrated ChatGPT into the prototype of ”GPTCOM-\nCARE” to address programming query problems. This integration allowed for\nthe generation of multiple source code solutions for the same query, which in-","subject":"","page":19,"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"20","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"keywords":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 90197032-2673-4e55-b99c-f9f3fe45e88a
encoding: FLOAT32
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{"chroma:document":"the generation of multiple source code solutions for the same query, which in-\ncreased the eﬃciency of software development. The results of their study demon-\nstrated the eﬀectiveness of using ChatGPT to improve the quality and diversity\nof code solutions, ultimately reducing the amount of time and eﬀort required for\nsoftware development.Wang et al. [83] proposed the chatCAD method, which\nutilizes large language models (LLMs) such as ChatGPT to enhance the out-\nput of multiple CAD networks for medical images, including diagnosis, lesion\nsegmentation, and report generation networks. The method generates sugges-\ntions in the form of a chat dialogue. The authors tested the eﬀectiveness of\nthe method on a randomly selected set of 300 cases from the MIMIC-CXR\ndataset, which included 50 cases each of cardiomegaly, edema, consolidation,\natelectasis, pleural eﬀusion, and no ﬁndings. Compared to CvT2DistilGPT2\nand R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":19,"page_label":"20","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","producer":"pdfTeX-1.40.21","trapped":"/False","keywords":"","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 3e2b2e27-6bb4-46bb-8a1b-b7fc6e87ee05
encoding: FLOAT32
metadata:
{"producer":"pdfTeX-1.40.21","chroma:document":"and R2GenCMN, chatCAD showed signiﬁcant advantages in RC and F1, while\nonly performing weaker than R2GenCMN in PR.\nIntegrating ChatGPT into applications will still present challenges. Firstly,\nChatGPT’s performance may be aﬀected by language barriers or diﬀerences\nin terminology between diﬀerent systems. Additionally, ChatGPT’s responses\nare not always deterministic, which poses a challenge when integrating with\nsystems that require precise and reproducible results. Finally, the processing\ntime of ChatGPT is slow for integration tasks involving time-sensitive data such\nas traﬃc, which is a limitation in time-critical environments.\n2.2 AI Ethics\nSince the advent of ChatGPT, this powerful natural language processing model\nhas not only brought great convenience to people but also triggered more crisis-\naware thinking. Some researchers have started to hypothesize and study the\npotential negative impacts of ChatGPT. This proactive research provides good","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":19,"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","creator":"LaTeX with hyperref","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"20","author":"","keywords":"","total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 07c81af7-9d58-41cd-9ef0-0fa5e5fcdbe2
encoding: FLOAT32
metadata:
{"author":"","creator":"LaTeX with hyperref","page_label":"20","page":19,"chroma:document":"potential negative impacts of ChatGPT. This proactive research provides good\nproposals for standardized construction to address future AI abuse issues.\nRegarding the evaluation of ChatGPT’s own political and ethical tendencies,\nHartmann et al. [84] used Wahl-O-Mat, one of the most commonly used voting\n20","producer":"pdfTeX-1.40.21","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d984c03a-869b-4fa5-8130-d17af4c8d4f8
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"title":"","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"advice applications in the world, to show ChatGPT political statements from\ndiﬀerent parties, forcing it to make choices of agree, disagree, or neutral. The\nresults indicated that ChatGPT has a pro-environment, left-wing liberal ide-\nology, which was also conﬁrmed in the nation-state agnostic political compass\ntest. Another study (referenced as [85]) examined ChatGPT’s moral standards\nby repeatedly asking it diﬀerent versions of the trolley problem, and found that\nChatGPT gave answers with diﬀerent moral orientations, lacking a ﬁrm moral\nstance. A subsequent test also found that ChatGPT’s lack of consistency could\naﬀect people’s moral judgments. Additionally, Borji et al. [86] demonstrated\nChatGPT’s inconsistency in reasoning, factual errors, mathematics, coding, and\nbias across eleven related aspects. These ﬁndings highlight ChatGPT’s inher-\nent traits and limitations, and people should be aware of their potential impact\nwhen seeking advice from ChatGPT.","trapped":"/False","page_label":"21","author":"","page":20,"producer":"pdfTeX-1.40.21","subject":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d799b34a-36be-4b57-9898-8bc1a8209827
encoding: FLOAT32
metadata:
{"page_label":"21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","producer":"pdfTeX-1.40.21","chroma:document":"when seeking advice from ChatGPT.\nRegarding relevant policies and regulations, Hacker et al. [87] discussed the\nnature and rules of large generative AI models, including ChatGPT, which are\nrapidly changing the way we communicate, explain, and create. The author\nsuggested that diﬀerent stakeholders in the value chain should take regulatory\nresponsibility and deploy four strategies to tailor more comprehensive laws for\nthe beneﬁt of society. Another study (referenced as [88]) criticized the European\nCommission’s proposal on AI responsibility and suggested revising the proposed\nAI responsibility framework to ensure eﬀective compensation while promoting\ninnovation, legal certainty, and sustainable AI regulation. A policy framework\nwas proposed (referenced as [89]) to customize LLMs, such as ChatGPT, in a so-\ncially acceptable and safe manner, emphasizing the need to align large language\nmodels (LLMs) with human preferences.","author":"","creator":"LaTeX with hyperref","page":20,"title":"","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"subject":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: e94d5d33-6bba-4bef-b585-461b573a6c50
encoding: FLOAT32
metadata:
{"author":"","total_pages":35,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","chroma:document":"models (LLMs) with human preferences.\nThe political and ethical tendencies of ChatGPT could inﬂuence users’ be-\nhavior and decision-making to some extent. However, some studies have con-\nducted in-depth research on the use of norms and limitations, which could enable\nhumans to use ChatGPT more reasonably and safely.\n2.3 Evaluation\n2.3.1 Comparison of ChatGPT with existing popular models\nWe use publicly available datasets to comprehensively evaluate the strengths\nand limitations of ChatGPT. Reference [90] evaluates the technical performance\nof ChatGPT in multitask, multilingual, and multimodal aspects based on 23\nstandard public datasets and newly designed multimodal datasets, including\neight diﬀerent common natural language processing application tasks. The ex-\nperimental results show that, in terms of multitasking, ChatGPT outperforms\nvarious state-of-the-art zero-shot learning large language models in most tasks,","page_label":"21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","page":20}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 699f39e6-5b1c-4df2-b26c-a3b6c8f75b95
encoding: FLOAT32
metadata:
{"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page":20,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"page_label":"21","author":"","title":"","subject":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"various state-of-the-art zero-shot learning large language models in most tasks,\nand even outperforms ﬁne-tuned task-speciﬁc models in some individual tasks.\nIn terms of multilingualism, we found that ChatGPT cannot be applied to low-\nresource languages because it cannot understand the language and generate\ntranslations for that language. In terms of multimodality, ChatGPT’s ability is\nstill basic compared to specialized language-visual models.\n21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 5fbf3609-6402-4f35-93f1-fd53db49cde0
encoding: FLOAT32
metadata:
{"page":21,"moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"22","title":"","chroma:document":"In terms of stability, reference [91] concludes that ChatGPT’s performance is\nalways lower than SOTA, the current state-of-the-art model, in almost all tasks.\nThis means that as a general model, ChatGPT has never reached the level of the\nbest existing models. Experimental data shows that the average quality of the\nSOTA model is 73.7%, while the average quality of the ChatGPT model is only\n56.5%. At the same time, ChatGPT’s stability is poor: the standard deviation\nof its performance is 23.3%, while the SOTA model’s standard deviation is\nonly 16.7%. This non-deterministic behavior exhibited by ChatGPT could be\na serious drawback in some problems.\nSimilarly, Qin et al. [92] conducted a comprehensive evaluation of whether\nChatGPT is a qualiﬁed general natural language processing task solver. The ex-\nperiment analyzed ChatGPT’s zero-shot learning ability based on 20 commonly\nused public datasets covering 7 representative task categories. Below, we will","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","trapped":"/False","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 39bcd2c5-e278-4d7a-842e-c754a4afca2d
encoding: FLOAT32
metadata:
{"page_label":"22","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","author":"","chroma:document":"used public datasets covering 7 representative task categories. Below, we will\nanalyze ChatGPT’s performance on each task:\nIn terms of reasoning tasks, ChatGPT performs average on mathematical\nsymbol, commonsense causal, and logical reasoning tasks, but performs well\nin arithmetic reasoning [92]. That is to say, ChatGPT’s abilities vary among\ndiﬀerent types of reasoning tasks. In terms of logical reasoning, ChatGPT’s\ndeductive and abductive reasoning are superior to inductive reasoning, while\nin other reasoning tasks, such as analogy, causal and commonsense reasoning,\nChatGPT performs well [90].\nIn terms of sentiment analysis task, ChatGPT performs similarly to GPT-3.5\nand bert-style models [92, 93]. However, according to literature [91], ChatGPT\nhas losses not exceeding 25% on most tasks, except for three relatively sub-\njective emotion perception tasks where it performs poorly. If we remove these\ntasks to calculate the average quality of the two models, we ﬁnd that the SOTA","page":21,"producer":"pdfTeX-1.40.21","total_pages":35,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","keywords":"","creator":"LaTeX with hyperref","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: d1251624-951a-4768-9013-67055a6d3792
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","subject":"","chroma:document":"tasks to calculate the average quality of the two models, we ﬁnd that the SOTA\nmethod has an average quality of 80%, while the ChatGPT method has an av-\nerage quality of 69.7%. That is to say, ChatGPT performs well on all tasks\nexcept for emotion-related tasks, and can handle most of the problems we con-\nsider. However, overall, its performance is lower than the SOTA model based\non experimental data, but the diﬀerence between the two is not very large.\nIn other tasks, according to literature [92], ChatGPT performs well in nat-\nural language inference, i.e., the task of inferring sentence relationships, and its\nperformance on this task is signiﬁcantly better than all bert-style models [93].\nHowever, while ChatGPT performs well on inference tasks, it may produce some\nself-contradictory or unreasonable responses, which is its potential limitation.\nIn question-answering, dialogue, and summarization tasks, ChatGPT performs","page_label":"22","author":"","keywords":"","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","page":21,"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: ede1e6cc-524e-483a-a07f-862549df9d48
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"22","title":"","author":"","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creator":"LaTeX with hyperref","total_pages":35,"page":21,"subject":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"In question-answering, dialogue, and summarization tasks, ChatGPT performs\nbetter than the GPT-3.5 model [92], especially in the question-answering task,\nwhere its performance is comparable to bert-style models [93]. Therefore, we\nhave demonstrated that ChatGPT is a qualiﬁed general-purpose model.\nHowever, ChatGPT also has limitations in many aspects. Firstly, it lacks the\nability to handle non-textual semantic reasoning tasks such as mathematical,\ntemporal, and spatial reasoning, and it performs poorly in multi-hop reasoning\n[90]. Secondly, ChatGPT is not good at solving named entity recognition tasks\n[92]. Furthermore, ChatGPT performs poorly in handling tasks involving nega-\ntive connotations and neutral similarity [93]. Finally, these conclusions indicate\n22","keywords":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2bea98d4-0c87-4321-89f2-720d880f249a
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","total_pages":35,"page_label":"23","page":22,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"that, like other large pre-trained language models, ChatGPT has limitations in\ncompleting complex reasoning tasks.\nIn summary, ChatGPT’s zero-shot performance is comparable to ﬁne-tuned\nbert and GPT-3.5 models, and with the help of advanced prompting strategies,\nChatGPT can demonstrate better comprehension abilities. However, it still\ncannot outperform the current SOTA models.\n2.3.2 The possibility of using ChatGPT for plagiarism and cheating\nIn response to the possibility of ChatGPT being used for plagiarism and cheat-\ning, Zhou et al. [94] reﬂected on the current state of development of artiﬁcial in-\ntelligence like ChatGPT. As ChatGPT becomes increasingly easy to obtain and\nscalable in text generation, there is a high likelihood that these technologies will\nbe used for plagiarism, including scientiﬁc literature and news sources, posing\na great threat to the credibility of various forms of news media and academic","author":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 0b28c620-acad-4cd5-8d05-06d5a25c5574
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"23","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"a great threat to the credibility of various forms of news media and academic\narticles. Some scholars are concerned that the end of paper as a meaningful\nevaluation tool may be approaching [95, 96], as ChatGPT can easily generate\npersuasive paragraphs, chapters, and papers on any given topic. Additionally,\nit will exacerbate plagiarism issues in many ﬁelds such as education, medicine,\nand law [10], and may be used for cheating in academic exams [97]. Deﬁnitional\nrecognition technology is a relatively eﬀective method for detecting plagiarism,\nand the deﬁnitional typology proposed in [94] can alleviate people’s concerns by\nbeing used to construct new datasets. Susnjak [97] proposed a solution to the\npossibility of large language models like ChatGPT being used for exam cheat-\ning: guiding ChatGPT to generate some critical thinking problems through\nquestioning, then providing answers and critically evaluating them. Analysis of","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","total_pages":35,"creator":"LaTeX with hyperref","subject":"","keywords":"","page":22,"title":"","author":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9fdd0c16-92ad-460c-93c5-0fe9040271b1
encoding: FLOAT32
metadata:
{"title":"","page_label":"23","page":22,"chroma:document":"questioning, then providing answers and critically evaluating them. Analysis of\nChatGPT shows that it exhibits critical thinking, can generate highly realistic\ntext in terms of accuracy, relevance, depth, breadth, logic, persuasiveness, and\noriginality. Therefore, educators must be aware of the possibility of ChatGPT\nbeing used for exam cheating and take measures to combat cheating behavior\nto ensure the fairness of online exams.\n2.3.3 Feedback from ChatGPT users\nIn response to feedback from ChatGPT users, Haque et al. [98] conducted a\nmixed-methods study using 10,732 early ChatGPT user tweets. The authors\nextracted Twitter data using Python and Twitter API and constructed the\nChatGPTTweet dataset, which contains 18k tweets. For each tweet, the au-\nthors collected information on text content, user location, occupation, veriﬁca-\ntion status, date of publication, and tags. Based on this dataset, the authors\nstudied the characteristics of early ChatGPT users, discussion topics related to","author":"","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","total_pages":35,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 9cdf9500-baa2-48dd-ae3f-fceb24ee4b31
encoding: FLOAT32
metadata:
{"page":22,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","keywords":"","chroma:document":"studied the characteristics of early ChatGPT users, discussion topics related to\nChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.\nFor RQ1, the authors found that early ChatGPT users had a diverse and wide\nrange of occupational backgrounds and geographical locations. For RQ2, the\nauthors identiﬁed nine topics related to ChatGPT, including its impact on soft-\nware development, entertainment and creativity, natural language processing,\n23","creationdate":"2023-04-05T00:33:07+00:00","title":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","page_label":"23","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","subject":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a5ad67eb-e8f8-449c-ab50-13de3e87ef84
encoding: FLOAT32
metadata:
{"total_pages":35,"chroma:document":"education, chatbot intelligence, business development, search engines, question-\nanswering tests, and future careers and opportunities. For RQ3, most early\nusers expressed positive sentiment toward topics such as software development\nand creativity, while only a few expressed concern about the potential misuse\nof ChatGPT.\n2.3.4 Adverse eﬀects of ChatGPT on users\nRegarding the negative eﬀects of ChatGPT on users, Luan et al. [99] studied\nthe psychological principles of ChatGPT, delved into the factors that attract\nusers’ attention, and revealed the impact of these factors on future learning.\nIn the post-pandemic era, teachers and students are both facing uncertainty\nin the teaching process and job pressures. Under these common constraints of\neducation and employment, educators and students must re-evaluate current\neducational methods and outcomes, as well as students’ future career devel-\nopment. Through question-and-answer exchanges with ChatGPT, people can","page":23,"author":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"24","trapped":"/False","creator":"LaTeX with hyperref","keywords":"","subject":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","creationdate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: aa4e2f05-56a7-42a0-87a6-143d602bd75c
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{"moddate":"2023-04-05T00:33:07+00:00","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page":23,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"24","chroma:document":"opment. Through question-and-answer exchanges with ChatGPT, people can\neasily obtain appropriate solutions or key information, thereby enhancing their\nmotivation, eliminating anxiety in learning, improving interest, and achieving\npsychological satisfaction. Subhash et al. [100] explored whether large language\nmodels have the ability to reverse user preferences. With the development of\npre-trained large language models, people are increasingly concerned about the\nability of these models to inﬂuence, persuade, and potentially manipulate user\npreferences in extreme cases. Therefore, the literature [100] roughly qualita-\ntively analyzed that adversarial behavior does lead to potential changes in user\npreferences and behaviors in dialogue systems. If we want to further quanti-\ntatively analyze the ability of large language models in this regard, additional\nstatistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations","total_pages":35}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"subject":"","page":23,"chroma:document":"statistical summary techniques need to be used for future research.\n3 Discussion\n3.1 Limitations\nDespite the remarkable capabilities of ChatGPT and GPT-4, it still faces certain\nlimitations. Some of these limitations include:\nOutdated Knowledge\nThe current models are trained on historical data (up to 2021), thereby\nlacking real-time comprehension of current aﬀairs. This is a critical concern\nin today’s information-explosion era, as the reliability of prior knowledge bases\nprogressively diminishes, potentially yielding inaccurate responses, especially in\nrapidly evolving domains such as jurisprudence and technology. Additionally,\nthese models are incapable of fact-checking while the training data is composed\nof content from various sources, some of which may be unreliable, which may\nresult in seemingly plausible yet nonsensical responses.\n24","title":"","author":"","creator":"LaTeX with hyperref","keywords":"","page_label":"24","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"subject":"","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","page_label":"25","page":24,"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","total_pages":35,"chroma:document":"Insuﬃcient Understanding\nWhile these models can interpret the majority of inquiries and contextual\nsituations, they occasionally encounter comprehension biases when addressing\nambiguous or contextually complex queries. Furthermore, in certain specialized\nﬁelds, the abundance of unique abbreviation exacerbates the models’ under-\nstanding challenges, resulting in incorrect and vacuous responses.\nEnergy Consumption\nThroughout the training and inference stages, these large-scale models re-\nquire signiﬁcant computational resources and electrical power, resulting in ele-\nvated energy consumption and signiﬁcant carbon emissions. Consequently, this\nrestricts their deployment and practical applications.\nMalicious Usage\nDespite OpenAI implementing a series of restrictions to mitigate model tox-\nicity, instances of users evading these constraints through meticulously designed\nprompts have emerged, inducing the model to produce unhealthy content or\neven using it for illicit commercial purposes.","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:24:38
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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{"creator":"LaTeX with hyperref","chroma:document":"even using it for illicit commercial purposes.\nBias and Discrimination\nDue to the inﬂuence of pre-training data, the models exhibit biases in polit-\nical, ideological, and other areas. The application of LLMs in public domains,\nsuch as education and publicity, should be approached with extreme caution.\nPrivacy and Data Security\nConcurrent with the expansion of users, protecting user privacy and data\nsecurity becomes increasingly important. In fact, ChatGPT was banned in\nItaly in early April due to privacy concerns. This is particularly relevant given\nthe models’ extensive collection of personal information and preferences during\ninteractions, and as future multimodal models, such as GPT-4, may frequently\nrequire users to upload private photos.\n3.2 Future Directions\nIn forthcoming research, the development of models based on ChatGPT and\nGPT-4 may focus on addressing these limitations to enhance their practical\napplications.","subject":"","author":"","page_label":"25","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","page":24,"total_pages":35}


