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.
Specifically, the reward model is provided with four different comparisons for a single prompt during training: positive 1 > positive 2 , positive > neutral, positive > negative, and neutral > negative. The positive response when compared with the neutral and the negative response is chosen randomly. This allows the reward model to be exposed to different granularity of the specific preference and give scores accordingly during PPO training.
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