Who cites «PEER: A Collaborative Language Model»
- Toolformer: Language Models Can Teach Themselves to Use ToolsConference on Neural Information Processing Systems (NeurIPS)Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.2023 · 2,065 citations
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- CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingarXiv:2305.11738Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.2023 · 326 citations
- Self-collaboration Code Generation via ChatGPTACM Transactions on Software Engineering and MethodologyYihong Dong; Xue Jiang; Zhi Jin;, Ge Li2023 · 319 citations
- Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
- Generating Sequences by Learning to Self-CorrectarXiv:2211.00053Sean Welleck, Ximing Lu, Peter West, et al.2022 · 172 citations
- When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsTransactions of the Association for Computational Linguistics (TACL)Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.2024 · 161 citations
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- MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language FeedbackarXiv:2309.10691Xingyao Wang, Zihan Wang, Jiateng Liu, et al.2023 · 141 citations
- Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction StrategiesTransactions of the Association for Computational Linguistics (TACL)Liangming Pan, Michael Saxon, Wenda Xu, et al.2024 · 140 citations
- Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-CollaborationarXiv:2307.05300Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.2023 · 140 citations
- LM vs LM: Detecting Factual Errors via Cross ExaminationConference on Empirical Methods in Natural Language Processing (EMNLP)Roi Cohen, May Hamri, Mor Geva, et al.2023 · 126 citations
- Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via DebateFindings of the Association for Computational LinguisticsKai Xiong, Xiao Ding, Yixin Cao, et al.2023 · 94 citations
- RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model OutputsAnnual Meeting of the Association for Computational Linguistics (ACL)Afra Feyza Akyurek, Ekin Akyurek, Ashwin Kalyan, et al.2023 · 89 citations
- Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language GenerationTransactions of the Association for Computational Linguistics (TACL)Patrick Fernandes, Aman Madaan, Emmy Liu, et al.2023 · 84 citations
- Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error CorrectionarXiv:2303.14342Steven Coyne, Keisuke Sakaguchi, Diana Galvan-Sosa, et al.2023 · 81 citations
- Self-Contrast: Better Reflection Through Inconsistent Solving PerspectivesAnnual Meeting of the Association for Computational Linguistics (ACL)Wenqi Zhang, Yongliang Shen, Linjuan Wu, et al.2024 · 75 citations
- Meta-Prompting: Enhancing Language Models with Task-Agnostic ScaffoldingarXiv:2401.12954Mirac Suzgun, Adam Tauman Kalai2024 · 62 citations
- GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local RefinementsarXiv:2402.10963Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.2024 · 45 citations
- ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer ReviewsAnnual Meeting of the Association for Computational Linguistics (ACL)Mike D'Arcy, Alexis Ross, Erin Bransom, et al.2024 · 32 citations
- PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model CorruptionsarXiv:2305.14908Anthony Chen, Panupong Pasupat, Sameer Singh, et al.2023 · 32 citations
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