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Intrinsic self-correct was a method that instructed large language models (LLMs) to verify and correct their responses without external feedback.
Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Retrieving and reading: A comprehensive survey on open-domain question answering
Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. 2021 · 2017
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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The role of "condition": A novel scientific knowledge graph representation and construction model
Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, and Meng Jiang. 2019 · 2019
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Earlier work this paper cites.
CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Language models are few-shot learners
OpenAI. 2020 · 2020
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Tri-train: Automatic pre-fine tuning between pre-training and fine-tuning for SciNER
Qingkai Zeng, Wenhao Yu, Mengxia Yu, Tianwen Jiang, Tim Weninger, and Meng Jiang. 2020 · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
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SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Biomedical knowledge graphs construction from conditional statements
Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, and Meng Jiang. 2021 · 2021
Cited alongside, same era.
Interscript: A dataset for interactive learning of scripts through error feedback
Niket Tandon, Aman Madaan, Peter Clark, Keisuke Sakaguchi, and Yiming Yang. 2021 · 2021
Cited alongside, same era.
Validating label consistency in NER data annotation
Qingkai Zeng, Mengxia Yu, Wenhao Yu, Tianwen Jiang, and Meng Jiang. 2021 · 2021
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
CodeRL: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Hoi. 2022 · 2022
Cited alongside, same era.
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, and Jianfeng Gao. 2023 · 2023
Later among the works it cites.
Self-prompted chain-of-thought on large language models for open-domain multi-hop reasoning
Jinyuan Wang, Junlong Li, and Hai Zhao. 2023a · 2023
Later among the works it cites.
Generating sequences by learning to self-correct
Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. 2023 · 2023
Later among the works it cites.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2023 · 2023
Later among the works it cites.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
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Rainier: Reinforced knowledge introspector for commonsense question answering
Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, and Yejin Choi. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
Cited alongside, same era.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2023 · 2023
Cited alongside, same era.
Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp
Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia. 2023 · 2023
Cited alongside, same era.
Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer. 2023 · 2023
Cited alongside, same era.
Making language models better reasoners with step-aware verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2024 · 2024
Closest in time.
Factool: Factuality detection in generative AI - a tool augmented framework for multi-task and multi-domain scenarios
I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. 2024 · 2024
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CRITIC: Large language models can self-correct with tool-interactive critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong, yelong shen, Yujiu Yang, Nan Duan, and Weizhu Chen. 2024 · 2024
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Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2024 · 2024
Closest in time.
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2024 · 2024
Closest in time.
Selfcheck: Using LLMs to zero-shot check their own step-by-step reasoning
Ning Miao, Yee Whye Teh, and Tom Rainforth. 2024 · 2024
Closest in time.
OpenAI. 2024 · 2024
Closest in time.
Instructing large language models to identify and ignore irrelevant conditions
Zhenyu Wu, Chao Shen, and Meng Jiang. 2024b · 2024
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