Multi-Persona Thinking for Bias Mitigation in Large Language Models

Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes.We propose Multi-Persona Thinking (MPT), a simple inferencetime framework that reduces social bias by encouraging reasoning from multiple perspectives.MPT guides the model to consider contrasting social identities, such as male and female, together with a neutral viewpoint.These viewpoints then interact through an iterative reasoning process to identify and correct biased judgments.This design transforms the potential weakness of persona assignment into a mechanism to mitigate bias.We evaluate MPT on two widely used bias benchmarks with both open-source and closed-source models.Our results show that MPT achieves a lower bias than the existing prompting-based methods while maintaining the core reasoning ability. 1