Personalized Soups: LLM Alignment Via Parameter Merging

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Personalized Soups: LLM Alignment Via Parameter Merging
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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. 4 EXPERIMENTS 4.

Finally, while P-MORL and P-SOUPS both outperform other methods on average, there exists a discrepancy between the simulated and human evaluation; P-SOUPS has the highest average win rate in GPT-4 evaluation while P-MORL has the highest in human evaluation. Nonetheless,P-SOUPS is able to show superior performance in comparison to baseline methods and competitive performance to P-MORL.

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