Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text

Amr Mohamed; Yang Zhang; Michalis Vazirgiannis; Guokan Shang

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arXiv · 2506.14012

Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text

Amr Mohamed·Yang Zhang·Michalis Vazirgiannis·Guokan Shang

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Abstract

Code-switching (CSW) is the act of alternating between two or more languages within a single discourse. This phenomenon is widespread in multilingual communities, and increasingly prevalent in online content, where users naturally mix languages in everyday communication. As a result, Large Language Models (LLMs), now central to content processing and generation, are frequently exposed to code-switched inputs. Given their widespread use, it is crucial to understand how LLMs process and reason about such mixed-language text. This paper presents a systematic evaluation of LLM comprehension under code-switching by generating CSW variants of established reasoning and comprehension benchmarks. While degradation is evident when foreign tokens disrupt English text$\unicode{x2013}$even under linguistic constraints$\unicode{x2013}$embedding English into other languages often improves comprehension. Though prompting yields mixed results, fine-tuning offers a more stable path to degradation mitigation.

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Amr Mohamed, Yang Zhang, Michalis Vazirgiannis, Guokan Shang. 2025-06-16. Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text. https://arxiv.org/abs/2506.14012

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