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arXiv · 2604.17632

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

Qingcheng Zeng·Yuheng Lu·Zeqi Zhou·Heli Qi·Puxuan Yu·Fuheng Zhao·Hitomi Yanaka·Weihao Xuan·Naoto Yokoya
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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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Qingcheng Zeng, Yuheng Lu, Zeqi Zhou, Heli Qi, Puxuan Yu, Fuheng Zhao, Hitomi Yanaka, Weihao Xuan, Naoto Yokoya. 2026-04-19. Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers. https://arxiv.org/abs/2604.17632

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