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arXiv · 2610.20272

CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing

Zewen Jiang·Yahan Li·Chenlu Guo·Diana Inkpen·Yuan Wu·Yi Chang
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Abstract

Large language models (LLMs) have demonstrated multilingual capabilities across diverse language generation and understanding tasks. Yet effective code-switching requires more than multilingual proficiency: models must decide when, where, and why to switch languages in ways that fit the surrounding context. This context-sensitive ability remains insufficiently evaluated. We introduce CSWBench, a benchmark for evaluating context-sensitive code-switching in creative writing through two complementary settings: Focused Narrative and Extended Narrative. We further propose CSWEval, a theory-informed framework with two complementary levels: event-level evaluation of Licensing, Contextual Appropriateness, and Functional Gain, and whole-text evaluation of task-specific quality---overall literary and narrative quality in Focused Narrative and quality emphasizing multilingual organization in Extended Narrative. Evaluating twelve LLMs, we find that models exhibit meaningful but uneven code-switching competence across narrative contexts. Model differences are particularly pronounced in multilingual literary integration, while multilingual-focused dimensions show stronger difficulty effects overall than the general narrative dimensions; narratively salient switches are associated with stronger contextual and functional realization; and event-level CS quality is moderately associated with whole-text performance.

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Zewen Jiang, Yahan Li, Chenlu Guo, Diana Inkpen, Yuan Wu, Yi Chang. 2026-10-14. CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing. https://arxiv.org/abs/2610.20272

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