解决多语言问答中对非英语的系统性偏见问题
All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG
- 设计无语言依赖的重排序机制，让多语言证据更公平参与
- 实验显示非英语表现提升显著，最高达18.7%的准确率改善
- 适合需要公平多语言支持的AI系统开发者使用
多语言检索增强生成（mRAG）利用跨语言知识来增强大模型的全球知识能力。然而我们发现，当前mRAG系统在重排序阶段存在语言偏见，系统性地偏好英语及查询母语。通过引入估计最优基准分析，我们量化了现有重排序器与可实现上限之间的显著性能差距。进一步分析揭示关键分布失配：最优预测需依赖多语言分散的证据，但当前系统却系统性抑制这些‘答案关键’文档，从而限制生成效果。为此，我们提出语言无关的、以生成效用驱动的重排序对齐方法LAURA，使多语言证据排名与下游生成目标一致。在多种语言和生成模型上的实验表明，LAURA有效缓解语言偏见，并持续提升mRAG性能。
原文摘要 · Abstract (English)
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.
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