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A safe and trustworthy use of Large Language Models (LLMs) requires an accurate expression of confidence in their answers.
Are humans good intuitive statisticians after all? rethinking some conclusions from the literature on judgment under uncertainty
Leda Cosmides and John Tooby · 1996
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Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits · 2009
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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Language models (mostly) know what they know, 2022
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan · 2022
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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau · 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, et al · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 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
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Gpt-4 technical report
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Samuel Joseph Amouyal, Tomer Wolfson, Ohad Rubin, Ori Yoran, Jonathan Herzig, and Jonathan Berant · 2023
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The internal state of an llm knows when it’s lying, 2023
Amos Azaria and Tom Mitchell · 2023
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Grounding large language models in interactive environments with online reinforcement learning
Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Cited alongside, same era.
A survey on large language models: Applications, challenges, limitations, and practical usage
Muhammad Usman Hadi, Rizwan Qureshi, Abbas Shah, Muhammad Irfan, Anas Zafar, Muhammad Bilal Shaikh, Naveed Akhtar, Jia Wu, Seyedali Mirjalili, et al · 2023
Cited alongside, same era.
Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao, Huaming Chen, Felix Juefei-Xu, and Lei Ma · 2023
Cited alongside, same era.
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
Cited alongside, same era.
Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Calibration-tuning: Teaching large language models to know what they don‘t know
Sanyam Kapoor, Nate Gruver, Manley Roberts, Arka Pal, Samuel Dooley, Micah Goldblum, and Andrew Wilson · 2024
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Taming overconfidence in llms: Reward calibration in rlhf
Jixuan Leng, Chengsong Huang, Banghua Zhu, and Jiaxin Huang · 2024
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Chops: Chat with customer profile systems for customer service with llms, 2024
Jingzhe Shi, Jialuo Li, Qinwei Ma, Zaiwen Yang, Huan Ma, and Lei Li · 2024
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Lacie: Listener-aware finetuning for confidence calibration in large language models
Elias Stengel-Eskin, Peter Hase, and Mohit Bansal · 2024
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Lawluo: A multi-agent collaborative framework for multi-round chinese legal consultation, 2024
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Potsawee Manakul, Adian Liusie, and Mark JF Gales · 2023
Cited alongside, same era.
Foundation models for generalist medical artificial intelligence
Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure Leskovec, Eric J Topol, and Pranav Rajpurkar · 2023
Cited alongside, same era.
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Cited alongside, same era.
Dialogue shaping: Empowering agents through npc interaction
Wei Zhou, Xiangyu Peng, and Mark Riedl · 2023
Cited alongside, same era.
Magda: Multi-agent guideline-driven diagnostic assistance
David Bani-Harouni, Nassir Navab, and Matthias Keicher · 2024
Cited alongside, same era.
Teaching large language models to express knowledge boundary from their own signals
Lida Chen, Zujie Liang, Xintao Wang, Jiaqing Liang, Yanghua Xiao, Feng Wei, Jinglei Chen, Zhenghong Hao, Bing Han, and Wei Wang · 2024
Cited alongside, same era.
Can ai assistants know what they don’t know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wenwei Zhang, Zhangyue Yin, Shimin Li, Linyang Li, Zhengfu He, Kai Chen, and Xipeng Qiu · 2024
Cited alongside, same era.
Jingyun Sun, Chengxiao Dai, Zhongze Luo, Yangbo Chang, and Yang Li · 2024
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When to trust llms: Aligning confidence with response quality
Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, Huawei Shen, and Bolin Ding · 2024
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Towards generalist biomedical ai
Tao Tu, Shekoofeh Azizi, Danny Driess, Mike Schaekermann, Mohamed Amin, Pi-Chuan Chang, Andrew Carroll, Charles Lau, Ryutaro Tanno, Ira Ktena, et al · 2024
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Calibrating large language models using their generations only
Dennis Ulmer, Martin Gubri, Hwaran Lee, Sangdoo Yun, and Seong Joon Oh · 2024
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Knowledge verification to nip hallucination in the bud
Fanqi Wan, Xinting Huang, Leyang Cui, Xiaojun Quan, Wei Bi, and Shuming Shi · 2024
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Calibration in deep learning: A survey of the state-of-the-art, 2024
Cheng Wang · 2024
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms, 2024
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi · 2024
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Hongshen Xu, Zichen Zhu, Situo Zhang, Da Ma, Shuai Fan, Lu Chen, and Kai Yu · 2024
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Alignment for honesty
Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, and Pengfei Liu · 2024
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R-tuning: Instructing large language models to say ‘i don’t know’
Hanning Zhang, Shizhe Diao, Yong Lin, Yi Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang · 2024
Later among the works it cites.
Radialog: Large vision-language models for x-ray reporting and dialog-driven assistance
Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Benedikt Wiestler, Nassir Navab, and Matthias Keicher · 2025
Closest in time.
Steerconf: Steering llms for confidence elicitation
Ziang Zhou, Tianyuan Jin, Jieming Shi, and Qing Li · 2025
Closest in time.