代码切换信息检索：基准测试、现状分析与现有检索系统的局限性

摘要

语码转换是全球交流中普遍存在的语言现象，然而现代信息检索系统仍主要基于单语环境进行设计与评估。为弥合这一关键鸿沟，我们开展了针对语码转换信息检索的系统性研究。通过人工标注构建的CSR-L（轻量版语码转换检索基准）数据集，真实还原了混合语言查询的自然特性。我们在统计模型、稠密检索和延迟交互三大范式下的实验表明，语码转换构成了基础性性能瓶颈，即使强大多语言模型的检索效能也会因此衰减。研究揭示这种失效源于纯语言文本与语码转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CS-MTEB综合基准，观察到系统性能最大降幅达27%。最后，我们验证了词汇扩展等标准多语言技术仍无法完全消除这些缺陷。这些发现揭示了现有系统的脆弱性，并将语码转换确立为未来信息检索优化的关键前沿领域。

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、现状分析与现有检索系统的局限性

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

摘要

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