代码切换信息检索：基准测试、分析与当前检索系统的局限

摘要

代码切换作为全球交流中普遍存在的语言现象，却面临着现代信息检索系统仍主要基于单语环境设计与评估的现实困境。为弥合这一关键断层，我们开展了针对代码切换信息检索的系统性研究。通过人工标注构建的CSR-L（轻量版代码切换检索基准）数据集，有效捕捉了混合语言查询的真实自然度。我们在统计、稠密和延迟交互三大范式下的实验表明，代码切换构成了基础性的性能瓶颈，即使强大多语言模型的检索效能也会因此受损。研究揭示这种失效源于纯语言文本与代码切换文本在嵌入空间中存在的显著差异。为进一步拓展研究维度，我们提出覆盖11类任务的综合基准CS-MTEB，观察到最高达27%的性能下降。最后实验证明，词汇扩展等标准多语言技术仍无法完全弥补这些缺陷。这些发现既揭示了现有系统的脆弱性，也确立了代码切换作为未来信息检索优化关键前沿的重要地位。

English

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.

代码切换信息检索：基准测试、分析与当前检索系统的局限

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

摘要

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