Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion

ArXi:2601.02956v2 Announce Type: replace Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, resulting in the widespread adoption of English pivoting. While prior studies attribute this advantage to the superior English-centric capabilities of Large Language Models (LLMs), we find that such measurements are significantly distorted by structural priors inherent in evaluation benchmarks.