All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG
Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao Institute of Software, Chinese Academy of Sciences, Xuanang Chen, Yaojie Lu Institute of Software, Chinese Academy of Sciences 等 12 位作者
Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However, we show that current mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language. By introducing an estimated oracle evidence analysis, we quantify a substantial performance gap between existing rerankers and the achievable upper bound. Further analysis reveals a critical distributional mismatch: while optimal predictions require evidence scattered across multiple languages, current systems systematically suppress such “answer-critical” documents, thereby limiting downstream generation performance. To bridge this gap, we propose Language-Agnostic Utility-driven Reranker Alignment (LAURA), Experiments across diverse languages and generation models show that LAURA effectively mitigates language bias and consistently improves mRAG performance.
尚无 AI 解读，展示原文摘要
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表