Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers

• URL: http://arxiv.org/abs/2604.17632v1
• Date: Sun, 19 Apr 2026 22:01:41 GMT
• Title: Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers
• Abstract summary: We introduce CSR-L (Code-Switching Retrieval benchmark-Lite), constructing a dataset via human annotation to capture the authentic naturalness of mixed-language queries.<n>We show that code-switching acts as a fundamental performance bottleneck, degrading the effectiveness of even robust multilingual models.
• Score: 32.12010196874932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monolingual contexts. To bridge this critical disconnect, we present a holistic study dedicated to code-switching IR. We introduce CSR-L (Code-Switching Retrieval benchmark-Lite), constructing a dataset via human annotation to capture the authentic naturalness of mixed-language queries. Our evaluation across statistical, dense, and late-interaction paradigms reveals that code-switching acts as a fundamental performance bottleneck, degrading the effectiveness of even robust multilingual models. We demonstrate that this failure stems from substantial divergence in the embedding space between pure and code-switched text. Scaling this investigation, we propose CS-MTEB, a comprehensive benchmark covering 11 diverse tasks, where we observe performance declines of up to 27%. Finally, we show that standard multilingual techniques like vocabulary expansion are insufficient to resolve these deficits completely. These findings underscore the fragility of current systems and establish code-switching as a crucial frontier for future IR optimization.

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