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Large Language Models (LLMs) excel in various natural language processing tasks but struggle with hallucination issues.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 1905
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Cosmos qa: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1909
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Solving general arithmetic word problems
Subhro Roy and Dan Roth. 2016 · 2016
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Earlier work this paper cites.
The box is in the pen: Evaluating commonsense reasoning in neural machine translation
Jie He, Tao Wang, Deyi Xiong, and Qun Liu. 2020 · 2020
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Hover: A dataset for many-hop fact extraction and claim verification
Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal. 2020 · 2020
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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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 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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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
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Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. 2022 · 2022
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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, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 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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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
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It’s not easy being wrong: Evaluating process of elimination reasoning in large language models
Nishant Balepur, Shramay Palta, and Rachel Rudinger. 2023 · 2023
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Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023 · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 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.
Look before you leap: An exploratory study of uncertainty measurement for large language models
Yuheng Huang, Jiayang Song, Zhijie Wang, Huaming Chen, and Lei Ma. 2023 · 2023
Cited alongside, same era.
Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
Cited alongside, same era.
Encouraging divergent thinking in large language models through multi-agent debate
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 Le, and Denny Zhou. 2023 · 2023
Later among the works it cites.
Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2023 · 2023
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Answering questions by meta-reasoning over multiple chains of thought
Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, and Jonathan Berant. 2023 · 2023
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Siren’s song in the ai ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023 · 2023
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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.
Score: A framework for self-contradictory reasoning evaluation
Ziyi Liu, Isabelle Lee, Yongkang Du, Soumya Sanyal, and Jieyu Zhao. 2023 · 2023
Cited alongside, same era.
Refiner: Reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. 2023 · 2023
Cited alongside, same era.
Llamas know what gpts don’t show: Surrogate models for confidence estimation
Vaishnavi Shrivastava, Percy Liang, and Ananya Kumar. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 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.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Cited alongside, same era.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
Later among the works it cites.
Why does chatgpt fall short in answering questions faithfully?
Shen Zheng, Jie Huang, and Kevin Chen-Chuan Chang. 2023 · 2023
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Zero-shot llm-guided counterfactual generation for text
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, and Huan Liu. 2024 · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, 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
Closest in time.
Moxin Li, Wenjie Wang, Fuli Feng, Fengbin Zhu, Qifan Wang, and Tat-Seng Chua. 2024 · 2024
Closest in time.
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. 2024 · 2024
Closest in time.
Llms for generating and evaluating counterfactuals: A comprehensive study
Van Bach Nguyen, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2024 · 2024
Closest in time.
Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
Closest in time.
Self-polish: Enhance reasoning in large language models via problem refinement
Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024 · 2024
Closest in time.
Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2024 · 2024
Closest in time.
Confidence calibration and rationalization for llms via multi-agent deliberation
Ruixin Yang, Dheeraj Rajagopa, Shirley Anugrah Hayati, Bin Hu, and Dongyeop Kang. 2024 · 2024
Closest in time.