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Language model calibration refers to the alignment between the confidence of the model and the actual performance of its responses.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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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
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 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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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F Liu, and Matt Gardner · 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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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Calibrating deep neural networks using focal loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, and Puneet Dokania · 2020
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Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Jize Zhang, Bhavya Kailkhura, and T Yong-Jin Han · 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
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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On the sensitivity of reward inference to misspecified human models
Joey Hong, Kush Bhatia, and Anca Dragan · 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
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Models of human preference for learning reward functions
W Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 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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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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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 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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Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, and Louis-Philippe Morency · 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
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Maxmin-rlhf: Towards equitable alignment of large language models with diverse human preferences
Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Furong Huang, Dinesh Manocha, Amrit Singh Bedi, and Mengdi Wang · 2024
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Rlhf workflow: From reward modeling to online rlhf
Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, and Tong Zhang · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Kto: Model alignment as prospect theoretic optimization, 2024
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela · 2024
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Enhancing confidence expression in large language models through learning from past experience
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A general theoretical paradigm to understand learning from human preferences, 2023
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, and Rémi Munos · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
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Amplify-instruct: Synthetically generated diverse multi-turn conversations for efficient llm training
Luigi Daniele and Suphavadeeprasit · 2023
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Great models think alike: Improving model reliability via inter-model latent agreement
Ailin Deng, Miao Xiong, and Bryan Hooi · 2023
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Steerlm: Attribute conditioned sft as an (user-steerable) alternative to rlhf
Yi Dong, Zhilin Wang, Makesh Sreedhar, Xianchao Wu, and Oleksii Kuchaiev · 2023
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Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava · 2023
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Camels in a changing climate: Enhancing lm adaptation with tulu 2, 2023
Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A. Smith, Iz Beltagy, and Hannaneh Hajishirzi · 2023
Cited alongside, same era.
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
Cited alongside, same era.
Haixia Han, Tingyun Li, Shisong Chen, Jie Shi, Chengyu Du, Yanghua Xiao, Jiaqing Liang, and Xin Lin · 2024
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Orpo: Monolithic preference optimization without reference model
Jiwoo Hong, Noah Lee, and James Thorne · 2024
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Openrlhf: An easy-to-use, scalable and high-performance rlhf framework
Jian Hu, Xibin Wu, Weixun Wang, Xianyu, Dehao Zhang, and Yu Cao · 2024
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Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2024
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Rewardbench: Evaluating reward models for language modeling, 2024
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi · 2024
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From crowdsourced data to high-quality benchmarks: Arena-hard and benchbuilder pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E Gonzalez, and Ion Stoica · 2024
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Skywork reward model series
Chris Yuhao Liu and Liang Zeng · 2024
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Provably mitigating overoptimization in rlhf: Your sft loss is implicitly an adversarial regularizer
Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose Blanchet, and Zhaoran Wang · 2024
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SimPO: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
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Simple-math: 2+2=4 4-1=3
Xavier Murias · 2024
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Offsetbias: Leveraging debiased data for tuning evaluators, 2024
Junsoo Park, Seungyeon Jwa, Meiying Ren, Daeyoung Kim, and Sanghyuk Choi · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 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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Codeultrafeedback: An llm-as-a-judge dataset for aligning large language models to coding preferences, 2024
Martin Weyssow, Aton Kamanda, and Houari Sahraoui · 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
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Advancing llm reasoning generalists with preference trees, 2024
Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, Zhenghao Liu, Bowen Zhou, Hao Peng, Zhiyuan Liu, and Maosong Sun · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
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