Mitigating Bias in Reinforcement Learning from Human Feedback for Large Language Models

Abstract

In this comprehensive study, we delve into the application of Reinforcement Learning from Human Feedback (RLHF) in fine-tuning large language models (LLMs) to align them with human preferences. We address the multifaceted challenges of potential bias in human feedback and propose robust mechanisms to mitigate these biases. Through extensive experimentation and analysis, we demonstrate the effectiveness of RLHF in refining model behaviors and improving alignment while ensuring adherence to ethical standards and diverse user expectations. Our research contributes to the ongoing effort to create more responsible and fair AI systems that can be safely deployed in various societal contexts.