Personalized Soups: LLM Alignment Via Parameter Merging - Abstract & Introduction

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Personalized Soups: LLM Alignment Via Parameter Merging - Abstract & Introduction
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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.

While Reinforcement Learning from Human Feedback aligns Large Language Models with general, aggregate human preferences, it is suboptimal for learning diverse, individual perspectives. In this work, we study Reinforcement Learning from Personalized Human Feedback problem, wherein LLMs are aligned to multiple preferences by modeling alignment as a Multi-Objective Reinforcement Learning problem.

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