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

• URL: http://arxiv.org/abs/2506.14012v1
• Date: Mon, 16 Jun 2025 21:19:27 GMT
• Title: Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text
• Authors: Amr Mohamed, Yang Zhang, Michalis Vazirgiannis, Guokan Shang,
• Abstract summary: Code-switching (CSW) is the act of alternating between two or more languages within a single discourse.<n>Large Language Models (LLMs) are now central to content and communication generation.
• Score: 25.05270733872823
• License: http://creativecommons.org/licenses/by/4.0/
• 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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