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This paper audits privacy risk in English-source multilingual RAG across five query languages, testing whether non-English queries increase PII leakage. Using a Qwen2.5-7B pipeline with two-stage defenses, it finds English has the highest point-estimate leak rate under output-only filtering, with residual leaks on Arabic and Swahili when the input judge is added.
Researchers identify systematic English and query-language bias in multilingual RAG rerankers and introduce LAURA, a utility-driven alignment method that boosts performance by retrieving answer-critical documents across languages.
A developer describes how French text in retrieved contexts caused their multilingual RAG system to unpredictably switch languages mid-answer, ultimately solved with a regex-based German detector and explicit negative prompts.