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Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks.
A cognitive process theory of writing
Flower, L.; and Hayes, J. R. 1981 · 1981
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Scaling laws for neural language models
Kaplan, J.; McCandlish, S.; Henighan, T.; Brown, T. B.; Chess, B.; Child, R.; Gray, S.; Radford, A.; Wu, J.; and Amodei, D. 2020 · 2001
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The Curious Case of Neural Text Degeneration
Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; and Choi, Y. 2020 · 2020
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Learning to summarize with human feedback
Stiennon, N.; Ouyang, L.; Wu, J.; Ziegler, D.; Lowe, R.; Voss, C.; Radford, A.; Amodei, D.; and Christiano, P. F. 2020 · 2020
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L.; and Liang, P. 2021 · 2021
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Constitutional ai: Harmlessness from ai feedback
Bai, Y.; Kadavath, S.; Kundu, S.; Askell, A.; Kernion, J.; Jones, A.; Chen, A.; Goldie, A.; Mirhoseini, A.; McKinnon, C.; et al. 2022 · 2022
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Large language models are zero-shot reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
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TruthfulQA: Measuring How Models Mimic Human Falsehoods
Lin, S.; Hilton, J.; and Evans, O. 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C. L.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; Schulman, J.; Hilton, J.; Kelton, F.; Miller, L.; Simens, M.; Askell, A.; Welinder, P.; Christiano, P.; Leike, J.; and Lowe, R. 2022 · 2022
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Generating Sequences by Learning to Self-Correct
Welleck, S.; Lu, X.; West, P.; Brahman, F.; Shen, T.; Khashabi, D.; and Choi, Y. 2022 · 2022
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Large language models are reasoners with self-verification
Weng, Y.; Zhu, M.; He, S.; Liu, K.; and Zhao, J. 2022 · 2022
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Re3: Generating Longer Stories With Recursive Reprompting and Revision
Yang, K.; Tian, Y.; Peng, N.; and Klein, D. 2022 · 2022
Cited alongside, same era.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Huang, Y.; Feng, X.; Feng, X.; and Qin, B. 2023 · 2023
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Self-Refine: Iterative Refinement with Self-Feedback
Madaan, A.; Tandon, N.; Gupta, P.; Hallinan, S.; Gao, L.; Wiegreffe, S.; Alon, U.; Dziri, N.; Prabhumoye, S.; Yang, Y.; Gupta, S.; Majumder, B. P.; Hermann, K.; Welleck, S.; Yazdanbakhsh, A.; and Clark, P. 2023 · 2023
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Manakul, P.; Liusie, A.; and Gales, M. J. F. 2023 · 2023
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REFINER: Reasoning Feedback on Intermediate Representations
Paul, D.; Ismayilzada, M.; Peyrard, M.; Borges, B.; Bosselut, A.; West, R.; and Faltings, B. 2023 · 2023
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Azaria, A.; and Mitchell, T. 2023 · 2023
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The capacity for moral self-correction in large language models
Ganguli, D.; Askell, A.; Schiefer, N.; Liao, T.; Lukošiūtė, K.; Chen, A.; Goldie, A.; Mirhoseini, A.; Olsson, C.; Hernandez, D.; et al. 2023 · 2023
Cited alongside, same era.
Scaling laws for reward model overoptimization
Gao, L.; Schulman, J.; and Hilton, J. 2023 · 2023
Cited alongside, same era.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Guo, B.; Zhang, X.; Wang, Z.; Jiang, M.; Nie, J.; Ding, Y.; Yue, J.; and Wu, Y. 2023 · 2023
Cited alongside, same era.
Finetuned Language Models Are Zero-Shot Learners
Wei, J.; Bosma, M.; Zhao, V. Y.; Guu, K.; Yu, A. W.; Lester, B.; Du, N.; Dai, A. M.; and Le, Q. V. 2022a
Cited in the paper.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022b
Cited in the paper.
PEER: A Collaborative Language Model
Schick, T.; Dwivedi-Yu, J.; Jiang, Z.; Petroni, F.; Lewis, P.; Izacard, G.; You, Q.; Nalmpantis, C.; Grave, E.; and Riedel, S. 2023 · 2023
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Reflexion: Language Agents with Verbal Reinforcement Learning
Shinn, N.; Cassano, F.; Labash, B.; Gopinath, A.; Narasimhan, K.; and Yao, S. 2023 · 2023
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Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
Suzgun, M.; Scales, N.; Schärli, N.; Gehrmann, S.; Tay, Y.; Chung, H. W.; Chowdhery, A.; Le, Q.; Chi, E.; Zhou, D.; and Wei, J. 2023 · 2023
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Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Zhou, D.; Schärli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Cui, C.; Bousquet, O.; Le, Q.; and Chi, E. 2023 · 2023
Later among the works it cites.