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The recent success of Large Language Models (LLMs) has catalyzed an increasing interest in their self-correction capabilities.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986 · 1986
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Earlier work this paper cites.
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, et al. 2021 · 2021
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2021
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Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, et al. 2022 · 2022
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. 2022 · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Controllable natural language generation with contrastive prefixes
Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen. 2022 · 2022
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Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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A survey of joint intent detection and slot filling models in natural language understanding
Henry Weld, Xiaoqi Huang, Siqu Long, Josiah Poon, and Soyeon Caren Han. 2022 · 2022
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Generating sequences by learning to self-correct
Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. 2022 · 2022
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Natural language processing for smart construction: Current status and future directions
Chengke Wu, Xiao Li, Yuanjun Guo, Jun Wang, Zengle Ren, Meng Wang, and Zhile Yang. 2022 · 2022
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Re3: Generating longer stories with recursive reprompting and revision
Kevin Yang, Yuandong Tian, Nanyun Peng, and Dan Klein. 2022 · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Rl4f: Generating natural language feedback with reinforcement learning for repairing model outputs
Afra Feyza Akyürek, Ekin Akyürek, Aman Madaan, Ashwin Kalyan, Peter Clark, Derry Wijaya, and Niket Tandon. 2023 · 2023
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors. 2023 · 2023
Cited alongside, same era.
BenchLMM: Benchmarking cross-style visual capability of large multimodal models
Rizhao Cai, Zirui Song, Dayan Guan, Zhenhao Chen, Xing Luo, Chenyu Yi, and Alex Kot. 2023 · 2023
Cited alongside, same era.
Selfevolve: A code evolution framework via large language models
Shuyang Jiang, Yuhao Wang, and Yu Wang. 2023 · 2023
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Instance-aware prompt learning for language understanding and generation
Feihu Jin, Jinliang Lu, Jiajun Zhang, and Chengqing Zong. 2023 · 2023
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Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
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I Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, Pengfei Liu, et al. 2023 · 2023
Cited alongside, same era.
Rephrase and respond: Let large language models ask better questions for themselves
Yihe Deng, Weitong Zhang, Zixiang Chen, and Quanquan Gu. 2023 · 2023
Cited alongside, same era.
Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. 2023 · 2023
Cited alongside, same era.
Bridging the gap: A survey on integrating (human) feedback for natural language generation
Patrick Fernandes, Aman Madaan, Emmy Liu, António Farinhas, Pedro Henrique Martins, Amanda Bertsch, José GC de Souza, Shuyan Zhou, Tongshuang Wu, Graham Neubig, et al. 2023 · 2023
Cited alongside, same era.
Baldur: Whole-proof generation and repair with large language models
Emily First, Markus Rabe, Talia Ringer, and Yuriy Brun. 2023 · 2023
Cited alongside, same era.
Improving language model negotiation with self-play and in-context learning from ai feedback
Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023 · 2023
Cited alongside, same era.
The capacity for moral self-correction in large language models
Deep Ganguli, Amanda Askell, Nicholas Schiefer, Thomas Liao, Kamilė Lukošiūtė, Anna Chen, Anna Goldie, Azalia Mirhoseini, Catherine Olsson, Danny Hernandez, et al. 2023 · 2023
Cited alongside, same era.
Rarr: Researching and revising what language models say, using language models
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al. 2023 · 2023
Cited alongside, same era.
Potsawee Manakul, Adian Liusie, and Mark JF Gales. 2023 · 2023
Later among the works it cites.
Flirt: Feedback loop in-context red teaming
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, and Rahul Gupta. 2023 · 2023
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Refiner: Reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. 2023 · 2023
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Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023 · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao. 2023 · 2023
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GPT-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. 2023 · 2023
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Llms cannot find reasoning errors, but can correct them!
Gladys Tyen, Hassan Mansoor, Peter Chen, Tony Mak, and Victor Cărbune. 2023 · 2023
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Fine-grained human feedback gives better rewards for language model training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023 · 2023
Later among the works it cites.
Learning to simulate natural language feedback for interactive semantic parsing
Hao Yan, Saurabh Srivastava, Yintao Tai, Sida I Wang, Wen-tau Yih, and Ziyu Yao. 2023 · 2023
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Selfee: Iterative self-revising llm empowered by self-feedback generation
Seonghyeon Ye, Yongrae Jo, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, and Minjoon Seo. 2023 · 2023
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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, et al. 2024 · 2024
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