Code-switching creates a fundamental performance bottleneck for multilingual retrievers, causing drops of up to 27% on new benchmarks CSR-L and CS-MTEB, with embedding divergence as the key cause and vocabulary expansion insufficient to fix it.
Evaluating Large Language Models for Cross-Lingual Retrieval
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XBCP benchmark shows deep research agents and multilingual retrievers lose accuracy, recall, calibration, and citation reliability when evidence is in non-English languages, even with gold evidence provided.
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Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers
Code-switching creates a fundamental performance bottleneck for multilingual retrievers, causing drops of up to 27% on new benchmarks CSR-L and CS-MTEB, with embedding divergence as the key cause and vocabulary expansion insufficient to fix it.
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Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus
XBCP benchmark shows deep research agents and multilingual retrievers lose accuracy, recall, calibration, and citation reliability when evidence is in non-English languages, even with gold evidence provided.