All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG

TL;DR

This paper identifies language bias in multilingual retrieval-augmented generation systems and introduces LAURA, a reranker that reduces bias and improves performance across languages.

Contribution

The paper presents LAURA, a novel language-agnostic reranker that aligns evidence ranking with generative utility to mitigate language bias in mRAG systems.

Findings

LAURA reduces language bias in mRAG systems.

LAURA improves performance across multiple languages.

Quantifies the performance gap caused by language bias.

Abstract

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 \textit{\textbf{L}anguage-\textbf{A}gnostic \textbf{U}tility-driven \textbf{R}eranker \textbf{A}lignment (LAURA)}, which aligns multilingual evidence ranking with downstream generative utility. Experiments across diverse languages and generation models show that LAURA effectively mitigates language bias and consistently improves mRAG performance.