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        <title>Multi-Persona Thinking for Bias Mitigation in Large Language Models</title>
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    <abstract>Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. We propose Multi-Persona Thinking (MPT), a simple inference-time 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.</abstract>
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%0 Conference Proceedings
%T Multi-Persona Thinking for Bias Mitigation in Large Language Models
%A Chen, Yuxing
%A Luo, Guoqing
%A Wu, Zijun
%A Mou, Lili
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-multi
%X Large Language Models (LLMs) exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. We propose Multi-Persona Thinking (MPT), a simple inference-time 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.
%U https://aclanthology.org/2026.findings-acl.1389/
%P 27895-27909

Markdown (Informal)

[Multi-Persona Thinking for Bias Mitigation in Large Language Models](https://aclanthology.org/2026.findings-acl.1389/) (Chen et al., Findings 2026)

• Multi-Persona Thinking for Bias Mitigation in Large Language Models (Chen et al., Findings 2026)

ACL

• Yuxing Chen, Guoqing Luo, Zijun Wu, and Lili Mou. 2026. Multi-Persona Thinking for Bias Mitigation in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 27895–27909, San Diego, California, United States. Association for Computational Linguistics.