# 语码转换信息检索：基准测试、分析与现有检索器的局限

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-19 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo9h6xfu02h8sls2t9fbb57q
- 原文链接：https://arxiv.org/abs/2604.17632

## AI 摘要

研究人员发布CSR-L和CS-MTEB两项基准测试以评估混合语言检索场景，后者涵盖11类任务。实验显示，语码转换使检索性能最高下降27%，其根源在于纯文本与混合文本在嵌入空间存在显著差异。即使采用词汇扩展等标准多语言技术也无法完全消除该缺陷，暴露出当前系统在处理自然混合语言查询时的结构性脆弱。

## 正文

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.