← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.2080 · 记录 #1
Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:46
摘要
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 online, exposing large language models (LLMs) to mixed-language inputs. We present a systematic evaluation of LLM comprehension under code-switching by generating linguistically grounded CSW variants of established benchmarks (Belebele, MMLU, XNLI) across five typologically diverse languages. Our contributions are: (i) a controlled pipeline for producing CSW test sets that respect linguistic constraints on code-switching; (ii) a multi-model, multi-language analysis showing that inserting non-English tokens into English consistently reduces accuracy on comprehension and reasoning benchmarks, whereas embedding English into non-English contexts often improves it; and (iii) a mitigation study contrasting in-context learning (ICL) with fine-tuning. Across model families, ICL cues yield inconsistent, and sometimes negative, effects, while fine-tuning on CSW data provides modest but reliable gains, partially recovering accuracy under CSW.
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记录 #1 · 2026-07 · 最新观测版本
Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text
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 online, exposing large language models (LLMs) to mixed-language inputs. We present a systematic evaluation of LLM comprehension under code-switching by generating linguistically grounded CSW variants of established benchmarks (Belebele, MMLU, XNLI) across five typologically diverse languages. Our contributions are: (i) a controlled pipeline for producing CSW test sets that respect linguistic constraints on code-switching; (ii) a multi-model, multi-language analysis showing that inserting non-English tokens into English consistently reduces accuracy on comprehension and reasoning benchmarks, whereas embedding English into non-English contexts often improves it; and (iii) a mitigation study contrasting in-context learning (ICL) with fine-tuning. Across model families, ICL cues yield inconsistent, and sometimes negative, effects, while fine-tuning on CSW data provides modest but reliable gains, partially recovering accuracy under CSW.
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