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Large language models (LLMs) are increasingly deployed via public-facing interfaces to interact with millions of users, each with diverse preferences.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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Artificial Intelligence, Values and Alignment
Iason Gabriel · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
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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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Artificial Intelligence, Humanistic Ethics
John Tasioulas · 2022
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Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, et al · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
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Suchin Gururangan, Dallas Card, Sarah K. Dreier, Emily K. Gade, Leroy Z. Wang, Zeyu Wang, Luke Zettlemoyer, and Noah A. Smith · 2022
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Fine-tuning language models to find agreement among humans with diverse preferences
Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, et al · 2022
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
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Out of One, Many: Using Language Models to Simulate Human Samples, September 2022
Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, and David Wingate · 2022
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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
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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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The History and Risks of Reinforcement Learning and Human Feedback, November 2023
Nathan Lambert, Thomas Krendl Gilbert, and Tom Zick · 2023
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The past, present and better future of feedback learning in large language models for subjective human preferences and values
Hannah Kirk, Andrew Bean, Bertie Vidgen, Paul Röttger, and Scott Hale · 2023
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DICES Dataset: Diversity in Conversational AI Evaluation for Safety, June 2023
Lora Aroyo, Alex S. Taylor, Mark Diaz, Christopher M. Homan, Alicia Parrish, Greg Serapio-Garcia, Vinodkumar Prabhakaran, and Ding Wang · 2023
Cited alongside, same era.
Pretraining language models with human preferences
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez · 2023
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The cultural psychology of large language models: Is chatgpt a holistic or analytic thinker?
Chuanyang Jin, Songyang Zhang, Tianmin Shu, and Zhihan Cui · 2023
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Personalized soups: Personalized large language model alignment via post-hoc parameter merging
Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, and Prithviraj Ammanabrolu · 2023
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Mmtom-qa: Multimodal theory of mind question answering
Chuanyang Jin, Yutong Wu, Jing Cao, Jiannan Xiang, Yen-Ling Kuo, Zhiting Hu, Tomer Ullman, Antonio Torralba, Joshua B Tenenbaum, and Tianmin Shu · 2024
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ChatGPT’s weekly users have doubled in less than a year
Emma Roth · 2024
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Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew Bean, Katerina Margatina, Juan Ciro, Rafael Mosquera, Max Bartolo, Adina Williams, He He, Bertie Vidgen, and Scott A. Hale · 2024
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Diverging preferences: When do annotators disagree and do models know?
Michael JQ Zhang, Zhilin Wang, Jena D Hwang, Yi Dong, Olivier Delalleau, Yejin Choi, Eunsol Choi, Xiang Ren, and Valentina Pyatkin · 2024
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Anand Siththaranjan, Cassidy Laidlaw, and Dylan Hadfield-Menell · 2023
Cited alongside, same era.
synthetic-instruct-gptj-pairwise (revision cc92d8d), 2023
Alex Havrilla · 2023
Cited alongside, same era.
Ultrafeedback: Boosting language models with high-quality feedback, 2023
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
Cited alongside, same era.
Are large language models good annotators?
Jay Mohta, Kenan Ak, Yan Xu, and Mingwei Shen · 2023
Cited alongside, same era.
Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
Cited alongside, same era.
Tao Feng, Chuanyang Jin, Jingyu Liu, Kunlun Zhu, Haoqin Tu, Zirui Cheng, Guanyu Lin, and Jiaxuan You · 2024
Cited alongside, same era.
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
Cited alongside, same era.
Simpo: Simple preference optimization with a reference-free reward
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
Cited alongside, same era.
Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, and Natasha Jaques · 2024
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Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Alexandre Rame, Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier, and Matthieu Cord · 2024
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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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Social Choice for AI Alignment: Dealing with Diverse Human Feedback, April 2024
Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, and William S. Zwicker · 2024
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Uncertainty-aware reward model: Teaching reward models to know what is unknown
Xingzhou Lou, Dong Yan, Wei Shen, Yuzi Yan, Jian Xie, and Junge 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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Automated social science: Language models as scientist and subjects
Benjamin S Manning, Kehang Zhu, and John J Horton · 2024
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Large language models cannot replace human participants because they cannot portray identity groups
Angelina Wang, Jamie Morgenstern, and John P Dickerson · 2024
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The illusion of artificial inclusion, February 2024
William Agnew, A. Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, and Kevin R. McKee · 2024
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A Roadmap to Pluralistic Alignment, February 2024
Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, and Yejin Choi · 2024
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STELA: a community-centred approach to norm elicitation for AI alignment
Stevie Bergman, Nahema Marchal, John Mellor, Shakir Mohamed, Iason Gabriel, and William Isaac · 2045
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