Work

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

Year: 2026

Type: conference-paper

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 gap, we present a holistic study of code-switching IR.We introduce the Code-Switching Retrieval benchmark-Lite (CSR-L), a human-annotated benchmark designed to capture natural mixedlanguage queries, and evaluate statistical, dense, cross-encoder, and late-interaction retrieval methods on it.The results show that code-switching is a persistent performance bottleneck, degrading even strong multilingual models.We further show that this failure is associated with substantial divergence between monolingual and code-switched query embeddings.To test whether the pattern generalizes beyond retrieval, we construct CS-MTEB, a benchmark covering 11 diverse tasks, where performance drops reach up to 27%.Finally, we examine lexicon-based vocabulary expansion and find that, while it yields partial gains, it does not close the gap to monolingual performance.These findings underscore the fragility of current systems and establish code-switching as a crucial frontier for future IR optimization.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.636