Lost in Translation: Measuring the Effect of Non-Native English on End User Performance of Large Language Models

Abstract:Large language models (LLMs) are increasingly used by people whose first language is not English, yet these users have been shown to receive systematically lower-quality responses than fluent speakers. Which specific features of non-native English drive this gap remains unclear, because fluency is itself a composite of mechanical accuracy, vocabulary use, organization, and discourse coherence. Here, we introduce FABLE, a controlled dataset of 190,911 English prompt variants derived from 174K real user prompts for writing-related tasks. Evaluating responses from 34 open-weight LLMs, we find a clear asymmetry; while models do not propagate surface errors such as misspellings into their outputs, models do mirror higher-level rhetorical and lexical qualities present in the user's prompt. Further, the overall quality of responses differs substantially between the least- and most-fluent prompts. These results highlight a key LLM performance disparity for non-native English LLM users, resulting in both lower-quality and less-fluent answers.

Computation and Language (cs.CL)

arXiv:2609.36214 [cs.CL]

(or arXiv:2609.36214v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.36214 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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