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

arXiv: https://arxiv.org/abs/2604.17632 · HTML: https://mlsanity.com/paper/2604.17632/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus](https://mlsanity.com/paper/2606.15345/) (2026)
- [The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness](https://mlsanity.com/paper/2605.28190/) (2026)

## Abstract (arXiv)

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.