Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate

TL;DR

This paper introduces a new bilingual benchmark and a multi-agent debate framework to evaluate and reduce Western-centric bias in large language models, demonstrating improved fairness across cultures without additional training.

Contribution

It presents CEBiasBench and Multi-Agent Cultural Debate (MACD), novel tools for explicit cultural bias evaluation and mitigation in LLMs, advancing cross-cultural fairness research.

Findings

MACD achieves 57.6% No Bias Rate on CEBiasBench

MACD outperforms baseline with 86.0% No Bias Rate

Framework generalizes to Arabic CAMeL benchmark

Abstract

Large language models (LLMs) exhibit systematic Western-centric bias, yet whether prompting in non-Western languages (e.g., Chinese) can mitigate this remains understudied. Answering this question requires rigorous evaluation and effective mitigation, but existing approaches fall short on both fronts: evaluation methods force outputs into predefined cultural categories without a neutral option, while mitigation relies on expensive multi-cultural corpora or agent frameworks that use functional roles (e.g., Planner--Critique) lacking explicit cultural representation. To address these gaps, we introduce CEBiasBench, a Chinese--English bilingual benchmark, and Multi-Agent Vote (MAV), which enables explicit ``no bias'' judgments. Using this framework, we find that Chinese prompting merely shifts bias toward East Asian perspectives rather than eliminating it. To mitigate such persistent bias, we propose Multi-Agent Cultural Debate (MACD), a training-free framework that assigns agents distinct cultural personas and orchestrates deliberation via a "Seeking Common Ground while Reserving Differences" strategy. Experiments demonstrate that MACD achieves 57.6% average No Bias Rate evaluated by LLM-as-judge and 86.0% evaluated by MAV (vs. 47.6% and 69.0% baseline using GPT-4o as backbone) on CEBiasBench and generalizes to the Arabic CAMeL benchmark, confirming that explicit cultural representation in agent frameworks is essential for cross-cultural fairness.