Impact of RLHF on Multilingual LLM Alignment in BUFFET Tasks

Abstract

Abstract: While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computational cost and complexity hamper its wider adoption. To alleviate some of the computational burden of fine-tuning, parameter efficient methods, like LoRA were introduced. In this work, we empirically evaluate the setup of Parameter Efficient Reinforcement Learning from Human Feedback (PE-RLHF) that leverages LoRA fine-tuning for Reward Modeling, and Reinforcement Learning. We benchmark the PE-RLHF setup on six diver

Research Question

How does reinforcement learning from human feedback (RLHF) influence the alignment of multilingual LLMs on BUFFET tasks, as evaluated by human preference scores versus baseline fine-tuning?

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