# embeddings_queue

created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","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","creationdate":"2023-04-05T00:33:07+00:00","page_label":"12","producer":"pdfTeX-1.40.21","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","page":11,"subject":"","trapped":"/False","total_pages":35,"keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"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","chroma:document":"engines. 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","title":"","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","page":11,"page_label":"12","producer":"pdfTeX-1.40.21","author":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e6287bc2-1a44-4b12-a61f-bbc045cc6fdc
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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","trapped":"/False","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page":11,"keywords":"","chroma:document":"et 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","page_label":"12","title":"","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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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","page_label":"12","author":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","page":11,"creator":"LaTeX with hyperref","chroma:document":"unable 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\ntasks. By analyzing the requirements for code generation, ChatGPT can pro-","total_pages":35,"title":"","subject":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e4b1d891-033e-4d8e-a3aa-05b544ae1b2f
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{"trapped":"/False","subject":"","creator":"LaTeX with hyperref","chroma:document":"duce 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","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","page":11,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7daa01ad-44dd-4bf8-88e1-7c38dd32077f
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{"total_pages":35,"page_label":"12","creator":"LaTeX with hyperref","keywords":"","title":"","page":11,"author":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","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","trapped":"/False","chroma:document":"such 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\nhighlight the diﬀerences between each solution using colors.Sobania et al. [40]","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 0c2702eb-2800-433b-aeff-fea745933ffb
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{"total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","title":"","page_label":"12","producer":"pdfTeX-1.40.21","creationdate":"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":"","chroma:document":"utilized 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","page":11,"creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":11,"title":"","author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","total_pages":35,"chroma:document":"and information, ChatGPT was able to ﬁx 31 bugs, demonstrating its potential\n12","keywords":"","page_label":"12","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 955df1f5-321f-4d49-a697-e7836eb54888
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{"creator":"LaTeX with hyperref","page_label":"13","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","subject":"","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","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":"","author":"","page":12,"moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"chroma:document":"obtained. 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","keywords":"","author":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"13","title":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","subject":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"page":12}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 26278dd9-c712-4744-bc0c-14f44436106d
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{"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","keywords":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"et 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","page":12,"title":"","page_label":"13","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":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 49062c85-3049-4d3f-9d0e-33a390898694
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{"title":"","chroma:document":"ChatGPT 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-","author":"","page":12,"trapped":"/False","page_label":"13","producer":"pdfTeX-1.40.21","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 516f0063-51fd-4683-b860-6a300e2c945f
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{"page":12,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creator":"LaTeX with hyperref","page_label":"13","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","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","total_pages":35,"title":"","chroma:document":"modiﬁcation, 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","author":"","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7a607d73-4ee1-499e-867e-d1326321ad0f
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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","total_pages":35,"chroma:document":"mimic 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","page_label":"13","author":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","subject":"","title":"","producer":"pdfTeX-1.40.21","page":12,"keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d8c74f48-be0f-4e36-8167-1878afb180b8
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{"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"13","author":"","subject":"","title":"","keywords":"","page":12,"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":"introductory 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,","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 0f77fe38-8e04-4dc0-b73d-cfd13b6ab624
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{"author":"","trapped":"/False","page":12,"producer":"pdfTeX-1.40.21","subject":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"creator":"LaTeX with hyperref","page_label":"13","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","chroma:document":"manual 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","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 83764854-a8d7-4f6a-b185-6c6ee88b469e
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{"page_label":"14","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","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":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","title":"","keywords":"","author":"","page":13,"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c5e76e13-cf48-46f9-879b-861ea30e4415
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{"creationdate":"2023-04-05T00:33:07+00:00","author":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","page":13,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"14","subject":"","title":"","total_pages":35,"chroma:document":"methods 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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: ea9a04c7-e204-4b3d-8177-bf225a8e95ce
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{"creator":"LaTeX with hyperref","keywords":"","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","subject":"","creationdate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","author":"","page_label":"14","chroma:document":"from 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","page":13}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b4589dc5-a1e0-4f59-85c1-3fb5a22966a3
encoding: FLOAT32
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{"subject":"","page":13,"trapped":"/False","producer":"pdfTeX-1.40.21","author":"","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","title":"","page_label":"14","total_pages":35,"chroma:document":"infer 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\ninferred emotion values from 0 to 1. The author visualized the process of how","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 2522a453-011b-49f7-acf6-c587d04c86ff
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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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":13,"creator":"LaTeX with hyperref","trapped":"/False","title":"","keywords":"","chroma:document":"the 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","creationdate":"2023-04-05T00:33:07+00:00","author":"","total_pages":35,"subject":"","producer":"pdfTeX-1.40.21","page_label":"14"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a34a2fc8-bddf-442e-b40e-65d5e110cf0b
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: be0046b9-e686-42e7-9bd2-3f315aa0a3c3
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{"moddate":"2023-04-05T00:33:07+00:00","keywords":"","trapped":"/False","producer":"pdfTeX-1.40.21","subject":"","page_label":"15","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","page":14,"chroma:document":"(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","creationdate":"2023-04-05T00:33:07+00:00","author":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 1f1e1ad2-5237-4cda-8a0f-0f0064a71fd0
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{"creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page_label":"15","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","chroma:document":"processing\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","page":14,"producer":"pdfTeX-1.40.21","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 815b6b63-42f9-4e2b-9dd6-2303da297136
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: ae7eb9fe-54e7-4a6b-b438-695f461e1e02
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{"page_label":"15","creator":"LaTeX with hyperref","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","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","keywords":"","chroma:document":"delete, 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","subject":"","producer":"pdfTeX-1.40.21","page":14,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 08ba4152-fbac-42f7-a9c9-3397da4502f6
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 49d89c3e-c0f4-4e13-a66c-25a5c6ed8aad
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{"page":15,"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.","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","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","subject":"","trapped":"/False","page_label":"16","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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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","title":"","author":"","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":15,"keywords":"","total_pages":35,"page_label":"16","subject":"","trapped":"/False","chroma:document":"gest 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-"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"page":15,"total_pages":35,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"models 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","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","page_label":"16","title":"","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","creationdate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 460e08d6-22ff-429d-8fb6-6114f678c6e1
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6ff10f42-7232-41c9-a405-21f915ad3d0a
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{"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-","page_label":"17","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","creationdate":"2023-04-05T00:33:07+00:00","page":16,"author":"","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","title":"","producer":"pdfTeX-1.40.21"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 247cbedd-f94e-4cca-9f6a-cbc274e486b9
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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","creationdate":"2023-04-05T00:33:07+00:00","moddate":"2023-04-05T00:33:07+00:00","page_label":"17","author":"","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","total_pages":35,"title":"","creator":"LaTeX with hyperref","chroma:document":"domly 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%","subject":"","producer":"pdfTeX-1.40.21","page":16}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e6b53791-aac5-4cb4-9658-15ed4c143036
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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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","author":"","keywords":"","creator":"LaTeX with hyperref","total_pages":35,"trapped":"/False","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","subject":"","page":16,"page_label":"17","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","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9d737266-a91e-48c8-9111-1de39f5d8e4f
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{"trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"2023-04-05T00:33:07+00:00","page_label":"17","keywords":"","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","page":16,"total_pages":35,"author":"","title":"","producer":"pdfTeX-1.40.21","chroma:document":"through 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","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: dcd40607-2a01-4be9-94c9-040d8f5dec6f
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{"keywords":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","chroma:document":"a 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-","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","moddate":"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","subject":"","producer":"pdfTeX-1.40.21","total_pages":35,"page_label":"17","page":16}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"page":16,"chroma:document":"lation 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,","subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"17","keywords":"","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","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"}


created_at: 2026-06-08 08:18:55
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{"title":"","moddate":"2023-04-05T00:33:07+00:00","page":16,"creator":"LaTeX with hyperref","author":"","subject":"","producer":"pdfTeX-1.40.21","total_pages":35,"page_label":"17","chroma:document":"and 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","creationdate":"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","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 28910c17-83ba-4854-af25-8be0fbe46302
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{"author":"","title":"","total_pages":35,"subject":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","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","page_label":"18","producer":"pdfTeX-1.40.21","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":17,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c59f3a68-8317-476b-91a9-bca12f47b96c
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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","creator":"LaTeX with hyperref","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":"18","keywords":"","total_pages":35,"chroma:document":"for 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","subject":"","page":17,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 563a2984-6b34-48d2-9210-17b94c2795de
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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","moddate":"2023-04-05T00:33:07+00:00","title":"","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.","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page_label":"18","author":"","subject":"","page":17,"trapped":"/False","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c569bd21-159b-4681-a416-402d6a03349c
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{"producer":"pdfTeX-1.40.21","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"ﬁelds.\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","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","page_label":"18","author":"","keywords":"","page":17,"title":"","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c4dcfa75-b29c-4e21-94dc-1b020080d9e8
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{"producer":"pdfTeX-1.40.21","chroma:document":"capability 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","subject":"","title":"","keywords":"","total_pages":35,"moddate":"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","trapped":"/False","page":17,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","author":"","page_label":"18"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a33c151b-6828-46ad-8ce0-710d661e246e
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{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"18","moddate":"2023-04-05T00:33:07+00:00","subject":"","title":"","total_pages":35,"page":17,"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","trapped":"/False","keywords":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","chroma:document":"keywords; 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.","author":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e09245f2-34a3-4624-bba5-bfb6c5c08e19
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{"moddate":"2023-04-05T00:33:07+00:00","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","subject":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page_label":"18","author":"","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","title":"","page":17,"creator":"LaTeX with hyperref","trapped":"/False","total_pages":35}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 4c4e9d47-8220-4522-a855-5f9d8b2f13cc
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{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","title":"","page":18,"keywords":"","subject":"","creator":"LaTeX with hyperref","page_label":"19","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":"","trapped":"/False","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 02d0417c-2350-49ad-a2ea-7e1fe03f61ff
encoding: FLOAT32
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{"author":"","producer":"pdfTeX-1.40.21","subject":"","total_pages":35,"page_label":"19","page":18,"creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"as 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-","title":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: ef6786c1-f1a4-44df-bec7-feac995fdb02
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{"page":18,"keywords":"","trapped":"/False","page_label":"19","author":"","total_pages":35,"subject":"","creationdate":"2023-04-05T00:33:07+00:00","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","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","producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3cfeba30-77b2-4100-bfd8-25f1bb3b9687
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{"creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"on 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-","producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"19","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","page":18,"total_pages":35,"keywords":"","subject":"","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 19c5ef85-5c9e-4081-a45c-752d5f8da9db
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{"page_label":"19","keywords":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"ing 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","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":18,"producer":"pdfTeX-1.40.21","subject":"","author":"","title":"","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}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5311077f-92c8-4e79-a4a6-c68b43201f48
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{"moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"creator":"LaTeX with hyperref","author":"","trapped":"/False","page":18,"subject":"","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"eﬃciently 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\nrecombine and transform ideas, and use genetic algorithms to iterate through","keywords":"","producer":"pdfTeX-1.40.21","page_label":"19","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: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e62b386e-52c2-4b65-84e2-4415de25a3a8
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{"subject":"","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","page_label":"19","author":"","page":18,"moddate":"2023-04-05T00:33:07+00:00","title":"","total_pages":35,"chroma:document":"complex 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","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b3cd8b91-e203-431e-a15d-eaa4b4202304
encoding: FLOAT32
metadata:
{"author":"","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","page":19,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"20","keywords":"","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","total_pages":35,"subject":"","moddate":"2023-04-05T00:33:07+00:00","title":"","trapped":"/False","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 453b40f1-fc7c-487f-8dc5-1a99c875a5a9
encoding: FLOAT32
metadata:
{"trapped":"/False","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","author":"","chroma:document":"systems. 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-","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","keywords":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":19,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","page_label":"20"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d4a3373f-214a-494b-994e-20081a38ee7f
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","page_label":"20","trapped":"/False","total_pages":35,"producer":"pdfTeX-1.40.21","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":19,"chroma:document":"creased 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","keywords":"","moddate":"2023-04-05T00:33:07+00:00","title":"","author":"","creator":"LaTeX with hyperref","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 95048474-15fd-4353-a554-9d6cec9264a1
encoding: FLOAT32
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{"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","page":19,"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,"author":"","producer":"pdfTeX-1.40.21","chroma:document":"segmentation, 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\nonly performing weaker than R2GenCMN in PR.","page_label":"20","subject":"","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6789facf-4832-46e5-951b-02a70d1fd19b
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","creationdate":"2023-04-05T00:33:07+00:00","page_label":"20","title":"","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","page":19,"trapped":"/False","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"only 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","subject":"","producer":"pdfTeX-1.40.21","keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 478a82b0-0c22-4004-9d30-8cb8b1b010e4
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","page_label":"20","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":"","producer":"pdfTeX-1.40.21","title":"","total_pages":35,"subject":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page":19,"chroma:document":"time 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":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 70d25af5-8524-4e4a-9445-02cc547d91d7
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"subject":"","title":"","page":19,"chroma:document":"proposals 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","keywords":"","trapped":"/False","page_label":"20","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 59118891-808c-4d69-b2e7-a8e74f3baa9c
encoding: FLOAT32
metadata:
{"title":"","page":20,"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","author":"","keywords":"","total_pages":35,"subject":"","page_label":"21","creator":"LaTeX with hyperref","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","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6c4efc9d-d636-4080-8620-f55523b885e2
encoding: FLOAT32
metadata:
{"trapped":"/False","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","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","page":20,"author":"","total_pages":35,"subject":"","page_label":"21","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"ChatGPT 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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6e4cc25d-c503-4be0-b408-996e8d39fc85
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{"trapped":"/False","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","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","subject":"","page":20,"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","producer":"pdfTeX-1.40.21","title":"","page_label":"21","total_pages":35,"keywords":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 6ebb36e3-c9fa-44da-95af-8c66a98426fe
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{"producer":"pdfTeX-1.40.21","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","subject":"","keywords":"","title":"","author":"","page":20,"page_label":"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,"moddate":"2023-04-05T00:33:07+00:00","chroma:document":"the 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","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: dadd01e9-d27c-4314-bd0e-2d916061d67a
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{"subject":"","title":"","producer":"pdfTeX-1.40.21","page":20,"page_label":"21","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","total_pages":35,"keywords":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","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","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 35f5f133-e9ca-4b98-be11-0a0fa4312041
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{"author":"","trapped":"/False","chroma:document":"and 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,","keywords":"","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":"","creationdate":"2023-04-05T00:33:07+00:00","page":20,"page_label":"21","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: fb3f3880-0e87-43d9-8e07-607eb52a99ae
encoding: FLOAT32
metadata:
{"creator":"LaTeX with hyperref","total_pages":35,"keywords":"","page":20,"page_label":"21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"and 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","creationdate":"2023-04-05T00:33:07+00:00","author":"","producer":"pdfTeX-1.40.21","title":"","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: fec0487f-d8e9-49ba-a641-f45413a83bee
encoding: FLOAT32
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{"title":"","total_pages":35,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"22","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","subject":"","creator":"LaTeX with hyperref","author":"","keywords":"","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","page":21}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: b5b496bf-76f7-4dfe-a340-cdd36806716c
encoding: FLOAT32
metadata:
{"creationdate":"2023-04-05T00:33:07+00:00","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","total_pages":35,"page":21,"subject":"","title":"","keywords":"","page_label":"22","trapped":"/False","chroma:document":"of 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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5825feb6-96de-4ce0-a235-9aeafc5585fe
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{"author":"","keywords":"","total_pages":35,"trapped":"/False","chroma:document":"analyze 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,","page_label":"22","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","page":21,"subject":"","title":"","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3f0e1584-64cd-439d-b7ad-3406d55488a7
encoding: FLOAT32
metadata:
{"trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"ChatGPT 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\nmethod has an average quality of 80%, while the ChatGPT method has an av-","creationdate":"2023-04-05T00:33:07+00:00","keywords":"","moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page":21,"author":"","page_label":"22","total_pages":35,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3faf0788-0f86-4438-8366-fc36653e2c2d
encoding: FLOAT32
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{"keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","chroma:document":"erage 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","moddate":"2023-04-05T00:33:07+00:00","author":"","subject":"","creator":"LaTeX with hyperref","title":"","trapped":"/False","total_pages":35,"page":21,"page_label":"22"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e74f9877-eab4-4ea8-bd29-af13ad7f9f60
encoding: FLOAT32
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{"page":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,"chroma:document":"performance 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\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","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","page_label":"22","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","title":"","author":"","subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: d0b856c6-fa8a-4f15-88a4-5bf86ac5b060
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"22","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":"have 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-","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","subject":"","trapped":"/False","page":21,"author":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","total_pages":35,"title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: cd6b4bce-38af-43be-bf45-7be3e9eb96a3
encoding: FLOAT32
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{"page":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","trapped":"/False","producer":"pdfTeX-1.40.21","chroma:document":"tive connotations and neutral similarity [93]. Finally, these conclusions indicate\n22","title":"","moddate":"2023-04-05T00:33:07+00:00","keywords":"","page_label":"22","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: a2a58146-7b5c-4d56-a3f1-1b20a7df379b
encoding: FLOAT32
metadata:
{"total_pages":35,"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","subject":"","title":"","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","producer":"pdfTeX-1.40.21","page":22,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","author":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 5e3916e5-73ed-4b9f-a4a5-09cba423fa82
encoding: FLOAT32
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{"keywords":"","page_label":"23","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","page":22,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","title":"","chroma:document":"In 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","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e59146e7-6e76-49f1-8c6a-8e932cf21eb6
encoding: FLOAT32
metadata:
{"title":"","producer":"pdfTeX-1.40.21","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","author":"","keywords":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","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","page_label":"23","subject":"","chroma:document":"articles. 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,","page":22}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 0a39b8e4-5bc0-49d2-9dfb-735f18dd12bc
encoding: FLOAT32
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{"producer":"pdfTeX-1.40.21","total_pages":35,"subject":"","page_label":"23","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":"","keywords":"","moddate":"2023-04-05T00:33:07+00:00","trapped":"/False","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","title":"","page":22,"chroma:document":"and 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\nChatGPT shows that it exhibits critical thinking, can generate highly realistic"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 3a11d449-8353-47da-a61e-f5535b0fc7c8
encoding: FLOAT32
metadata:
{"trapped":"/False","subject":"","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","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"text 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","producer":"pdfTeX-1.40.21","total_pages":35,"page_label":"23","creator":"LaTeX with hyperref","author":"","keywords":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":22}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 7cb961ac-8b8f-4263-89f6-b93552a78318
encoding: FLOAT32
metadata:
{"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","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","moddate":"2023-04-05T00:33:07+00:00","keywords":"","title":"","chroma:document":"extracted 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\nChatGPT on Twitter, and the sentiment of Twitter users toward ChatGPT.","page_label":"23","page":22,"author":"","producer":"pdfTeX-1.40.21","subject":"","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 70e0d9c7-4d7a-4cf4-a845-e71a46e36cb5
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{"producer":"pdfTeX-1.40.21","title":"","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","page_label":"23","trapped":"/False","author":"","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":22,"subject":"","chroma:document":"For 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","creator":"LaTeX with hyperref"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: e9f095a7-0b35-40c3-bbcb-7aa4b3d4f64e
encoding: FLOAT32
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{"trapped":"/False","keywords":"","total_pages":35,"subject":"","page":23,"title":"","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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","moddate":"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","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","page_label":"24"}


created_at: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"page":23,"creator":"LaTeX with hyperref","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","page_label":"24","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","title":"","subject":"","chroma:document":"ability 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":"","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"author":"","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"3 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","creator":"LaTeX with hyperref","subject":"","producer":"pdfTeX-1.40.21","total_pages":35,"trapped":"/False","keywords":"","page_label":"24","page":23,"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: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
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{"trapped":"/False","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-","keywords":"","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","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","author":"","page":24,"moddate":"2023-04-05T00:33:07+00:00","subject":"","producer":"pdfTeX-1.40.21","page_label":"25"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","total_pages":35,"page":24,"subject":"","chroma:document":"quire 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","trapped":"/False","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","author":"","keywords":"","page_label":"25","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creator":"LaTeX with hyperref","author":"","trapped":"/False","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":24,"page_label":"25","keywords":"","moddate":"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","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","total_pages":35,"subject":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"author":"","subject":"","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"keywords":"","chroma:document":"Italy 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.","page_label":"25","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","moddate":"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","trapped":"/False","producer":"pdfTeX-1.40.21","page":24}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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created_at: 2026-06-08 08:18:55
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{"subject":"","chroma:document":"intelligence (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.","page_label":"26","title":"","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","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","page":25,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creator":"LaTeX with hyperref","total_pages":35,"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","page_label":"26","page":25,"keywords":"","subject":"","author":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","producer":"pdfTeX-1.40.21","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","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 1890c1c5-4696-4949-9660-a92c7a2696c0
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created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 8f5b21c4-8ede-426a-bbfa-956cbf22a4aa
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{"keywords":"","trapped":"/False","author":"","subject":"","chroma:document":"stance, 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","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page":25,"creationdate":"2023-04-05T00:33:07+00:00","page_label":"26","moddate":"2023-04-05T00:33:07+00:00","total_pages":35,"creator":"LaTeX with hyperref","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"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: c2d450d0-53a7-4300-9a02-2d69dd1e8f18
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{"creator":"LaTeX with hyperref","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","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","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","producer":"pdfTeX-1.40.21","page":26,"subject":"","total_pages":35,"page_label":"27","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
id: 9b39e539-d2f2-4fd5-9827-e41de7739aa4
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{"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","author":"","creator":"LaTeX with hyperref","keywords":"","page":26,"moddate":"2023-04-05T00:33:07+00:00","title":"","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","total_pages":35,"chroma:document":"ethical 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_label":"27","trapped":"/False"}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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{"creationdate":"2023-04-05T00:33:07+00:00","chroma:document":"Language 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.","total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","keywords":"","trapped":"/False","page":26,"author":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"27","subject":"","creator":"LaTeX with hyperref","producer":"pdfTeX-1.40.21","title":""}


created_at: 2026-06-08 08:18:55
topic: persistent://default/default/0735af16-f3ba-42a1-bdee-bb6db86ef6df
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