This paper introduces RLPHF, which aligns large language models with personalized human preferences via multi-objective RL and parameter merging.
This paper is under CC 4.0 license. available on arxiv Authors: Joel Jang, CarperAI,University of Washington & Allen Institute for AI; Seungone Kim, KAIST AI; Yizhong Wang, University of Washington; Jack Hessel, University of Washington; Luke Zettlemoyer, Aleph Alpha; Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; Yejin Choi, UC San Diego.
Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie. Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, and Minjoon Seo. Exploring the benefits of training expert language models over instruction tuning. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett , Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 14702–14729.
David Silver, Satinder Singh, Doina Precup, and Richard S Sutton. Reward is enough. Artificial Intelligence, 299:103535, 2021. Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett. A long way to go: Investigating length correlations in rlhf. arXiv preprint arXiv:2310.03716, 2023. Feifan Song, Bowen Yu, Minghao Li, Haiyang Yu, Fei Huang, Yongbin Li, and Houfeng Wang. Preference ranking optimization for human alignment. arXiv preprint arXiv:2306.17492, 2023.
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