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Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs) by having multiple agents discuss solutions to a problem over several rounds of debate.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Unsupervised quality estimation for neural machine translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 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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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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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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Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models
Alfonso Amayuelas, Liangming Pan, Wenhu Chen, and William Wang. 2023 · 2023
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Chateval: Towards better llm-based evaluators through multi-agent debate
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. 2023 · 2023
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Reconcile: Round-table conference improves reasoning via consensus among diverse llms
Justin Chih-Yao Chen, Swarnadeep Saha, and Mohit Bansal. 2023 · 2023
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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. 2023 · 2023
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Shifting attention to relevance: Towards the uncertainty estimation of large language models
Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu. 2023 · 2023
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Lm-polygraph: Uncertainty estimation for language models
Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, and Artem Shelmanov. 2023 · 2023
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Metagpt: Meta programming for multi-agent collaborative framework
Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. 2023 · 2023
Cited alongside, same era.
Towards mitigating LLM hallucination via self reflection
Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung. 2023 · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
Cited alongside, same era.
Do large language models know what they don’t know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. 2023 · 2023
Later among the works it cites.
Exploring collaboration mechanisms for llm agents: A social psychology view
Jintian Zhang, Xin Xu, and Shumin Deng. 2023 · 2023
Later among the works it cites.
Semantically diverse language generation for uncertainty estimation in language models
Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi, and Sepp Hochreiter. 2024 · 2024
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Llama 3 model card
AI@Meta. 2024 · 2024
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Do llms know about hallucination? an empirical investigation of llm’s hidden states
Hanyu Duan, Yi Yang, and Kar Yan Tam. 2024 · 2024
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Camel: Communicative agents for" mind" exploration of large scale language model society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023 · 2023
Cited alongside, same era.
Encouraging divergent thinking in large language models through multi-agent debate
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, and Shuming Shi. 2023 · 2023
Cited alongside, same era.
Generating with confidence: Uncertainty quantification for black-box large language models
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. 2023 · 2023
Cited alongside, same era.
Mitigating hallucination in large multi-modal models via robust instruction tuning
Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2023 · 2023
Cited alongside, same era.
Let models speak ciphers: Multiagent debate through embeddings
Chau Pham, Boyi Liu, Yingxiang Yang, Zhengyu Chen, Tianyi Liu, Jianbo Yuan, Bryan A Plummer, Zhaoran Wang, and Hongxia Yang. 2023 · 2023
Cited alongside, same era.
A survey of hallucination in large foundation models
Vipula Rawte, Amit Sheth, and Amitava Das. 2023 · 2023
Cited alongside, same era.
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023 · 2023
Cited alongside, same era.
Hybrid uncertainty quantification for selective text classification in ambiguous tasks
Artem Vazhentsev, Gleb Kuzmin, Akim Tsvigun, Alexander Panchenko, Maxim Panov, Mikhail Burtsev, and Artem Shelmanov. 2023 · 2023
Cited alongside, same era.
Closest in time.
Fact-checking the output of large language models via token-level uncertainty quantification
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, et al. 2024 · 2024
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Don’t hallucinate, abstain: Identifying llm knowledge gaps via multi-llm collaboration
Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, and Yulia Tsvetkov. 2024 · 2024
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Towards uncertainty-aware language agent
Jiuzhou Han, Wray Buntine, and Ehsan Shareghi. 2024 · 2024
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Learning to trust your feelings: Leveraging self-awareness in llms for hallucination mitigation
Yuxin Liang, Zhuoyang Song, Hao Wang, and Jiaxing Zhang. 2024 · 2024
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Reducing llm hallucination using knowledge distillation: A case study with mistral large and mmlu benchmark
Daniel McDonald, Rachael Papadopoulos, and Leslie Benningfield. 2024 · 2024
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OpenAI. 2024 · 2024
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Towards detecting llms hallucination via markov chain-based multi-agent debate framework
Xiaoxi Sun, Jinpeng Li, Yan Zhong, Dongyan Zhao, and Rui Yan. 2024 · 2024
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Sayself: Teaching llms to express confidence with self-reflective rationales
Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, and Jing Gao. 2024 · 2024
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To believe or not to believe your llm
Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, and Csaba Szepesvári. 2024 · 2024
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Luq: Long-text uncertainty quantification for llms
Caiqi Zhang, Fangyu Liu, Marco Basaldella, and Nigel Collier. 2024 · 2024
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