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Aligning language models (LMs) to human preferences has emerged as a critical pursuit, enabling these models to better serve diverse user needs.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry · 1952
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Eine informationstheoretische ungleichung und ihre anwendung auf beweis der ergodizitaet von markoffschen ketten
Imre Csiszár · 1964
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A general class of coefficients of divergence of one distribution from another
S. M. Ali and S. D. Silvey · 1966
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On information-type measure of difference of probability distributions and indirect observations
Imre Csiszár · 1967
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Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
E. Zitzler and L. Thiele · 1999
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Probability: Theory and Examples, 4th Edition
Rick Durrett · 2010
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Linear Algebra
S.H. Friedberg, A.J. Insel, and L.E. Spence · 2014
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Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom B. 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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Human-aligned artificial intelligence is a multiobjective problem
Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin, and Jane Mummery · 2017
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Attention is all you need
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Lectures on Convex Optimization
Yurii Nesterov · 2018
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Improving language understanding by generative pre-training, 2018
Alec Radford and Karthik Narasimhan · 2018
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Fine-tuning language models from human preferences
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano · 2020
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Trl: Transformer reinforcement learning
Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert, and Shengyi Huang · 2020
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Preference-based reinforcement learning with finite-time guarantees
Yichong Xu, Ruosong Wang, Lin Yang, Aarti Singh, and Artur Dubrawski · 2020
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Controlled text generation as continuous optimization with multiple constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, and Yulia Tsvetkov · 2021
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Controllable generation from pre-trained language models via inverse prompting
Xu Zou, Da Yin, Qingyang Zhong, Hongxia Yang, Zhilin Yang, and Jie Tang · 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, Nicholas Joseph, Saurav Kadavath, John Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Christopher Olah, Benjamin Mann, and Jared Kaplan · 2022
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Pd-morl: Preference-driven multi-objective reinforcement learning algorithm
Toygun Basaklar, Suat Gumussoy, and Ümit Y. Ogras · 2022
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Gradient-based constrained sampling from language models
Sachin Kumar, Biswajit Paria, and Yulia Tsvetkov · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. 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
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COLD decoding: Energy-based constrained text generation with langevin dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt · 2022
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A general theoretical paradigm to understand learning from human preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, and Rémi Munos · 2023
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Slic-hf: Sequence likelihood calibration with human feedback
Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, and Peter J. Liu · 2023
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Calibrating sequence likelihood improves conditional language generation
Yao Zhao, Mikhail Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, and Peter J Liu · 2023
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Beyond one-preference-for-all: Multi-objective direct preference optimization for language models
Zhanhui Zhou, Jie Liu, Chao Yang, Jing Shao, Yu Liu, Xiangyu Yue, Wanli Ouyang, and Yu Qiao · 2023
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Scaling pareto-efficient decision making via offline multi-objective rl
Baiting Zhu, Meihua Dang, and Aditya Grover · 2023
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Self-play fine-tuning converts weak language models to strong language models
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Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, J’er’emy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco di Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, and Dylan Hadfield-Menell · 2023
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SteerLM: Attribute conditioned SFT as an (user-steerable) alternative to RLHF
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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
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Personalized soups: Personalized large language model alignment via post-hoc parameter merging
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Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Juntao Dai, Xuehai Pan, Chi Zhang, Ce Bian, Chi Zhang, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2023
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Llm-blender: Ensembling large language models with pairwise ranking and generative fusion
Dongfu Jiang, Xiang Ren, and Bill Yuchen Lin · 2023
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Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu · 2024
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Controllable preference optimization: Toward controllable multi-objective alignment
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Offset unlearning for large language models
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Tuning language models by proxy
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Decoding-time realignment of language models
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Keeping llms aligned after fine-tuning: The crucial role of prompt templates
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Pack of llms: Model fusion at test-time via perplexity optimization
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WARM: on the benefits of weight averaged reward models
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Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond
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Beyond reverse KL: Generalizing direct preference optimization with diverse divergence constraints
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Probabilistic inference in language models via twisted sequential monte carlo, 2024
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Weak-to-strong jailbreaking on large language models
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Free from bellman completeness: Trajectory stitching via model-based return-conditioned supervised learning
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