# embeddings_queue

created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a9648697-eb15-4fbf-acfd-9b1a07f78b67
encoding: FLOAT32
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{"title":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":25,"creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","trapped":"/False","page_label":"26","chroma:document":"industries such as K-12 education and journalism.\nFinally, the inﬂuence of ChatGPT and GPT-4 should not be limited to\njust the NLP ﬁeld. They also show promising prospects in the areas of com-\nputer vision, brain-inspired AI, and robotics. These models exhibit a capacity\nfor learning and comprehension comparable with human-level intelligence, po-\nsitioning them as a pivotal component in the development of artiﬁcial general\nintelligence (AGI)[101]. Their ability to facilitate seamless interactions between\nhumans and robots paves the way for the execution of more complex tasks.\nThe remarkable capacity of zero-shot in-context learning of these models en-\nables quick adaptation to new tasks without the requirement for labeled data\nfor ﬁne-tuning, which is a critical challenge in ﬁelds like medical informatics[102]\nand robotics[103] where the availability of labeled data is commonly limited or\nnon-existent.\n4 Conclusion","subject":"","author":"","creationdate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: f14aa39d-2bc0-453e-b8e0-e10db2bff771
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{"trapped":"/False","producer":"pdfTeX-1.40.21","keywords":"","subject":"","chroma:document":"non-existent.\n4 Conclusion\nThis review paper provides a comprehensive survey of ChatGPT and GPT-4,\nhighlighting their potential applications and signiﬁcant contributions to the ﬁeld\nof natural language processing. The ﬁndings of this study reveal that the interest\nin these models is growing rapidly, and they have shown considerable potential\nfor application across a wide range of domains. One key factor contributing\nto the success of ChatGPT and GPT-4 is their ability to perform large-scale\npre-training, which captures knowledge from the vast expanse of the internet,\nallowing the models to learn from a massive amount of data. The integration of\nReinforcement Learning from Human Feedback (RLHF) has further enhanced\nthe model’s adaptability and performance, making it highly eﬃcient in process-\ning natural language. This study has also identiﬁed several potential ethical\nconcerns related to the development and use of ChatGPT and GPT-4. For in-","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","page_label":"26","creationdate":"2023-04-05T00:33:07+00:00","page":25,"title":"","total_pages":35,"creator":"LaTeX with hyperref"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a1bfa72d-350a-4632-8746-89770a1696c9
encoding: FLOAT32
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{"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","page":25,"creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","chroma:document":"concerns related to the development and use of ChatGPT and GPT-4. For in-\nstance, there are concerns about the generation of biased or harmful content,\nprivacy violations, and the potential for misuse of the technology. It is crucial\nto address these concerns and ensure that ChatGPT and GPT-4 are developed\nand used in a responsible and ethical manner. Furthermore, the results of this\nstudy demonstrate that there is signiﬁcant potential for ChatGPT and GPT-4\n26","page_label":"26","title":"","total_pages":35,"creator":"LaTeX with hyperref","keywords":"","subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: bb9c00d1-5401-4de9-a4de-ec544f8b9d74
encoding: FLOAT32
metadata:
{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","creationdate":"2023-04-05T00:33:07+00:00","trapped":"/False","title":"","author":"","keywords":"","page":26,"total_pages":35,"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"to be applied in a range of domains, including education, history, mathematics,\nphysics, and more. These models can facilitate tasks such as generating sum-\nmaries, answering questions, and providing personalized recommendations to\nusers. Overall, the insights presented in this review paper can serve as a useful\nguide for researchers and practitioners looking to advance the ﬁeld of natural\nlanguage processing. Future research in this ﬁeld should focus on addressing\nethical concerns, exploring new applications, and ensuring the responsible use\nof ChatGPT and GPT-4. The potential of these models to revolutionize natural\nlanguage processing is enormous, and we look forward to seeing more develop-\nments in this ﬁeld.\nReferences\n[1] Radford A, Narasimhan K, Salimans T, Sutskever I, et al. Improving\nlanguage understanding by generative pre-training. OpenAI. 2018.\n[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.","subject":"","page_label":"27","producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: a0cd3513-a638-4db2-a047-898f462698e1
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{"page_label":"27","total_pages":35,"moddate":"2023-04-05T00:33:07+00:00","page":26,"trapped":"/False","creator":"LaTeX with hyperref","chroma:document":"[2] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.\nLanguage models are unsupervised multitask learners. OpenAI blog.\n2019;1(8):9.\n[3] Radford A, Wu J, Amodei D, Amodei D, Clark J, Brundage M, et al. Bet-\nter language models and their implications. OpenAI Blog https://openai\ncom/blog/better-language-models. 2019;1(2).\n[4] Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al.\nLanguage models are few-shot learners. Advances in neural information\nprocessing systems. 2020;33:1877-901.\n[5] Ouyang L, Wu J, Jiang X, Almeida D, Wainwright CL, Mishkin P, et al.\nTraining language models to follow instructions with human feedback.\narXiv preprint arXiv:220302155. 2022.\n[6] Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep\nreinforcement learning from human preferences. Advances in neural infor-\nmation processing systems. 2017;30.\n[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-","author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","keywords":"","creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","title":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6850474f-ae33-41f8-ac8a-21782ab93958
encoding: FLOAT32
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{"author":"","creator":"LaTeX with hyperref","page_label":"27","total_pages":35,"title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","keywords":"","producer":"pdfTeX-1.40.21","subject":"","chroma:document":"[7] Frieder S, Pinchetti L, Griﬃths RR, Salvatori T, Lukasiewicz T, Pe-\ntersen PC, et al. Mathematical capabilities of chatgpt. arXiv preprint\narXiv:230113867. 2023.\n[8] Pardos ZA, Bhandari S. Learning gain diﬀerences between ChatGPT and\nhuman tutor generated algebra hints. arXiv preprint arXiv:230206871.\n2023.\n[9] Shakarian P, Koyyalamudi A, Ngu N, Mareedu L. An Independent Eval-\nuation of ChatGPT on Mathematical Word Problems (MWP). arXiv\npreprint arXiv:230213814. 2023.\n27","creationdate":"2023-04-05T00:33:07+00:00","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":26}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 4e15f663-9794-4292-8923-cf672971f959
encoding: FLOAT32
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{"title":"","trapped":"/False","keywords":"","author":"","total_pages":35,"producer":"pdfTeX-1.40.21","page":27,"page_label":"28","moddate":"2023-04-05T00:33:07+00:00","chroma:document":"[10] Lehnert K. AI Insights into Theoretical Physics and the Swampland Pro-\ngram: A Journey Through the Cosmos with ChatGPT. arXiv preprint\narXiv:230108155. 2023.\n[11] Kortemeyer G. Could an Artiﬁcial-Intelligence agent pass an introductory\nphysics course? arXiv preprint arXiv:230112127. 2023.\n[12] West CG. AI and the FCI: Can ChatGPT Project an Understanding of\nIntroductory Physics? arXiv preprint arXiv:230301067. 2023.\n[13] Nov O, Singh N, Mann DM. Putting ChatGPT’s Medical Advice to the\n(Turing) Test. medRxiv. 2023.\n[14] Tu R, Ma C, Zhang C. Causal-Discovery Performance of ChatGPT in the\ncontext of Neuropathic Pain Diagnosis. arXiv preprint arXiv:230113819.\n2023.\n[15] Glymour C, Zhang K, Spirtes P. Review of Causal Discovery Methods\nBased on Graphical Models. Frontiers in Genetics. 2019.\n[16] Guo S, Wang Y, Li S, Saeed N. Semantic Communications with Ordered\nImportance using ChatGPT. arXiv preprint arXiv:230207142. 2023.","creationdate":"2023-04-05T00:33:07+00:00","subject":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 897d273b-6871-48c0-9fb4-edfaa4b723bc
encoding: FLOAT32
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{"creationdate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","author":"","total_pages":35,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","trapped":"/False","page":27,"keywords":"","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","chroma:document":"Importance using ChatGPT. arXiv preprint arXiv:230207142. 2023.\n[17] Wang S, Scells H, Koopman B, Zuccon G. Can chatgpt write a good\nboolean query for systematic review literature search? arXiv preprint\narXiv:230203495. 2023.\n[18] Kuzman T, Mozetic I, Ljubeˇ sic N. ChatGPT: Beginning of an End of\nManual Linguistic Data Annotation? Use Case of Automatic Genre Iden-\ntiﬁcation. arXiv e-prints. 2023:arXiv-2303.\n[19] Amin MM, Cambria E, Schuller BW. Will Aﬀective Computing Emerge\nfrom Foundation Models and General AI? A First Evaluation on Chat-\nGPT. arXiv preprint arXiv:230303186. 2023.\n[20] Zhang B, Ding D, Jing L. How would Stance Detection Techniques Evolve\nafter the Launch of ChatGPT? arXiv preprint arXiv:221214548. 2022.\n[21] Huang F, Kwak H, An J. Is ChatGPT better than Human Annota-\ntors? Potential and Limitations of ChatGPT in Explaining Implicit Hate\nSpeech. arXiv preprint arXiv:230207736. 2023.\n[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-","subject":"","creator":"LaTeX with hyperref","page_label":"28","title":"","moddate":"2023-04-05T00:33:07+00:00"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 87f7b850-fbb3-4a16-8734-a6999f0c6ffa
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","trapped":"/False","author":"","keywords":"","page_label":"28","moddate":"2023-04-05T00:33:07+00:00","title":"","creator":"LaTeX with hyperref","total_pages":35,"producer":"pdfTeX-1.40.21","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","subject":"","chroma:document":"[22] Zhang X, Chowdhury RR, Hong D, Gupta RK, Shang J. Model-\ning Label Semantics Improves Activity Recognition. arXiv preprint\narXiv:230103462. 2023.\n[23] Fu Q, Teng Z, Georgaklis M, White J, Schmidt DC. NL2CMD: An Up-\ndated Workﬂow for Natural Language to Bash Commands Translation.\narXiv preprint arXiv:230207845. 2023.\n28","creationdate":"2023-04-05T00:33:07+00:00","page":27}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: b44fd2f0-2e00-4106-88b1-f6c81c61df9e
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{"total_pages":35,"keywords":"","producer":"pdfTeX-1.40.21","trapped":"/False","subject":"","creationdate":"2023-04-05T00:33:07+00:00","page_label":"29","page":28,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","title":"","chroma:document":"[24] Chen Y, Eger S. Transformers go for the LOLs: Generating (humourous)\ntitles from scientiﬁc abstracts end-to-end. arXiv preprint arXiv:221210522.\n2022.\n[25] Chen N, Wang Y, Jiang H, Cai D, Chen Z, Li J. What would Harry\nsay? Building Dialogue Agents for Characters in a Story. arXiv preprint\narXiv:221106869. 2022.\n[26] Jeblick K, Schachtner B, Dexl J, Mittermeier A, St¨ uber AT, Topalis J,\net al. ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case\nStudy on Simpliﬁed Radiology Reports. arXiv preprint arXiv:221214882.\n2022.\n[27] Xia CS, Zhang L. Conversational automated program repair. arXiv\npreprint arXiv:230113246. 2023.\n[28] Jiao W, ZhaopengTu WJtX. Is ChatGPT A Good Translator? Yes With\nGPT-4 As The Engine.\n[29] Prieto SA, Mengiste ET, de Soto BG. Investigating the Use of\nChatGPT for the Scheduling of Construction Projects. Buildings.\n2023 mar;13(4):857. Available from: https://doi.org/10.3390%\n2Fbuildings13040857.","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","author":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 69579748-b925-4b04-ac05-736d1a3c2295
encoding: FLOAT32
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{"ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","moddate":"2023-04-05T00:33:07+00:00","subject":"","page":28,"producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","title":"","author":"","page_label":"29","keywords":"","creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"2023 mar;13(4):857. Available from: https://doi.org/10.3390%\n2Fbuildings13040857.\n[30] Michail A, Konstantinou S, Clematide S. UZH CLyp at SemEval-2023\nTask 9: Head-First Fine-Tuning and ChatGPT Data Generation for\nCross-Lingual Learning in Tweet Intimacy Prediction. arXiv preprint\narXiv:230301194. 2023.\n[31] Wang J, Liang Y, Meng F, Li Z, Qu J, Zhou J. Cross-Lingual Summa-\nrization via ChatGPT. arXiv preprint arXiv:230214229. 2023.\n[32] Yang X, Li Y, Zhang X, Chen H, Cheng W. Exploring the limits of\nchatgpt for query or aspect-based text summarization. arXiv preprint\narXiv:230208081. 2023.\n[33] Belouadi J, Eger S. ByGPT5: End-to-End Style-conditioned Po-\netry Generation with Token-free Language Models. arXiv preprint\narXiv:221210474. 2022.\n[34] Blanco-Gonzalez A, Cabezon A, Seco-Gonzalez A, Conde-Torres D,\nAntelo-Riveiro P, Pineiro A, et al. The Role of AI in Drug Discovery: Chal-\nlenges, Opportunities, and Strategies. arXiv preprint arXiv:221208104.\n2022.","trapped":"/False","total_pages":35}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 536192e3-e395-4f8f-8351-63e8e9e52ac5
encoding: FLOAT32
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{"keywords":"","trapped":"/False","page_label":"29","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","subject":"","moddate":"2023-04-05T00:33:07+00:00","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","creator":"LaTeX with hyperref","page":28,"author":"","chroma:document":"lenges, Opportunities, and Strategies. arXiv preprint arXiv:221208104.\n2022.\n[35] Khalil M, Er E. Will ChatGPT get you caught? Rethinking of plagiarism\ndetection. arXiv preprint arXiv:230204335. 2023.\n[36] Basic Z, Banovac A, Kruzic I, Jerkovic I. Better by you, better than\nme, chatgpt3 as writing assistance in students essays. arXiv preprint\narXiv:230204536. 2023.\n29","producer":"pdfTeX-1.40.21"}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 1cac6a6e-9d55-4d41-a8bf-cb27ed8744b4
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{"trapped":"/False","creator":"LaTeX with hyperref","page_label":"30","producer":"pdfTeX-1.40.21","page":29,"source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"[37] Noever D, Ciolino M. The Turing Deception. arXiv preprint\narXiv:221206721. 2022.\n[38] Megahed FM, Chen YJ, Ferris JA, Knoth S, Jones-Farmer LA. How\nGenerative AI models such as ChatGPT can be (Mis) Used in SPC Prac-\ntice, Education, and Research? An Exploratory Study. arXiv preprint\narXiv:230210916. 2023.\n[39] Treude C. Navigating Complexity in Software Engineering: A Prototype\nfor Comparing GPT-n Solutions. arXiv preprint arXiv:230112169. 2023.\n[40] Sobania D, Briesch M, Hanna C, Petke J. An analysis of the automatic\nbug ﬁxing performance of chatgpt. arXiv preprint arXiv:230108653. 2023.\n[41] Noever D, McKee F. Numeracy from Literacy: Data Science as an Emer-\ngent Skill from Large Language Models. arXiv preprint arXiv:230113382.\n2023.\n[42] McKee F, Noever D. Chatbots in a Botnet World. arXiv preprint\narXiv:221211126. 2022.\n[43] McKee F, Noever D. Chatbots in a Honeypot World. arXiv preprint\narXiv:230103771. 2023.\n[44] Susnjak T. Applying BERT and ChatGPT for Sentiment Analysis of","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","subject":"","author":"","moddate":"2023-04-05T00:33:07+00:00","keywords":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 90c1e9d7-3038-4b46-b3e0-943b1e941c25
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{"moddate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","trapped":"/False","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","chroma:document":"arXiv:230103771. 2023.\n[44] Susnjak T. Applying BERT and ChatGPT for Sentiment Analysis of\nLyme Disease in Scientiﬁc Literature. arXiv preprint arXiv:230206474.\n2023.\n[45] Tang Z, Kejriwal M. A Pilot Evaluation of ChatGPT and DALL-E 2 on\nDecision Making and Spatial Reasoning. arXiv preprint arXiv:230209068.\n2023.\n[46] Ortega-Mart´ ın M, Garc´ ıa-Sierra´O, Ardoiz A, ´Alvarez J, Armenteros JC,\nAlonso A. Linguistic ambiguity analysis in ChatGPT. arXiv preprint\narXiv:230206426. 2023.\n[47] Maddigan P, Susnjak T. Chat2vis: Generating data visualisations via\nnatural language using chatgpt, codex and gpt-3 large language models.\narXiv preprint arXiv:230202094. 2023.\n[48] Luo Y, Tang J, Li G. nvBench: A Large-Scale Synthesized Dataset for\nCross-Domain Natural Language to Visualization Task. arXiv preprint\narXiv:211212926. 2021.\n[49] Liu C, Han Y, Jiang R, Yuan X. Advisor: Automatic visualization answer\nfor natural-language question on tabular data. In: 2021 IEEE 14th Paciﬁc","page":29,"page_label":"30","keywords":"","producer":"pdfTeX-1.40.21","creationdate":"2023-04-05T00:33:07+00:00","total_pages":35,"title":"","author":"","subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 6e7fcd0b-41f1-497d-b4b5-8e2d50655604
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{"author":"","page_label":"30","chroma:document":"for natural-language question on tabular data. In: 2021 IEEE 14th Paciﬁc\nVisualization Symposium (PaciﬁcVis). IEEE; 2021. p. 11-20.\n30","producer":"pdfTeX-1.40.21","total_pages":35,"creationdate":"2023-04-05T00:33:07+00:00","page":29,"creator":"LaTeX with hyperref","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","title":"","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","keywords":"","subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
id: 2aff1ee9-dba7-4d7b-b01a-4c6b5703f832
encoding: FLOAT32
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{"author":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf","creationdate":"2023-04-05T00:33:07+00:00","creator":"LaTeX with hyperref","moddate":"2023-04-05T00:33:07+00:00","producer":"pdfTeX-1.40.21","page_label":"31","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","trapped":"/False","total_pages":35,"chroma:document":"[50] Narechania A, Srinivasan A, Stasko J. NL4DV: A toolkit for generat-\ning analytic speciﬁcations for data visualization from natural language\nqueries. IEEE Transactions on Visualization and Computer Graphics.\n2020;27(2):369-79.\n[51] Wei X, Cui X, Cheng N, Wang X, Zhang X, Huang S, et al. Zero-\nShot Information Extraction via Chatting with ChatGPT. arXiv preprint\narXiv:230210205. 2023.\n[52] Takanobu R, Zhang T, Liu J, Huang M. A hierarchical framework for\nrelation extraction with reinforcement learning. In: Proceedings of the\nAAAI conference on artiﬁcial intelligence. vol. 33; 2019. p. 7072-9.\n[53] Li S, He W, Shi Y, Jiang W, Liang H, Jiang Y, et al. Duie: A large-\nscale chinese dataset for information extraction. In: Natural Language\nProcessing and Chinese Computing: 8th CCF International Conference,\nNLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part\nII 8. Springer; 2019. p. 791-800.\n[54] Wang Z, Shang J, Liu L, Lu L, Liu J, Han J. Crossweigh: Training named","keywords":"","page":30,"subject":""}


created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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created_at: 2026-05-29 09:32:49
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{"author":"","subject":"","total_pages":35,"chroma:document":"[61] Du X, Cardie C. Event extraction by answering (almost) natural ques-\ntions. arXiv preprint arXiv:200413625. 2020.\n[62] Tang R, Han X, Jiang X, Hu X. Does Synthetic Data Generation of LLMs\nHelp Clinical Text Mining? arXiv preprint arXiv:230304360. 2023.\n[63] He J, Wang L, Hu Y, Liu N, Liu H, Xu X, et al. ICL-D3IE: In-Context\nLearning with Diverse Demonstrations Updating for Document Informa-\ntion Extraction. arXiv preprint arXiv:230305063. 2023.\n[64] Jaume G, Ekenel HK, Thiran JP. Funsd: A dataset for form under-\nstanding in noisy scanned documents. In: 2019 International Conference\non Document Analysis and Recognition Workshops (ICDARW). vol. 2.\nIEEE; 2019. p. 1-6.\n[65] Park S, Shin S, Lee B, Lee J, Surh J, Seo M, et al. CORD: a consoli-\ndated receipt dataset for post-OCR parsing. In: Workshop on Document\nIntelligence at NeurIPS 2019; 2019. .\n[66] Huang Z, Chen K, He J, Bai X, Karatzas D, Lu S, et al. Icdar2019 com-","creationdate":"2023-04-05T00:33:07+00:00","page":31,"trapped":"/False","moddate":"2023-04-05T00:33:07+00:00","keywords":"","producer":"pdfTeX-1.40.21","page_label":"32","creator":"LaTeX with hyperref","ptex.fullbanner":"This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2","title":"","source":"./data/Summary of ChatGPTGPT-4 Research.pdf"}


created_at: 2026-05-29 09:32:49
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created_at: 2026-05-29 09:32:49
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created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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created_at: 2026-05-29 09:32:49
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created_at: 2026-05-29 09:32:49
topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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created_at: 2026-05-29 09:32:49
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created_at: 2026-05-29 09:32:49
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topic: persistent://default/default/1862453a-1fb5-40bd-8b25-6a17f78a85ad
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created_at: 2026-05-29 09:32:49
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