Federico Marcuzzi, Martin Christoph, Iryna Gurevych, Alla Rozovskays · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29896 · open-access PDF
Existing social bias benchmarks evaluate large language models mostly on fluent, well-formed English, reflecting native speaker usage and leaving non-native writers unrepresented. To address this gap, we investigate how grammatical errors by non-native writers affect social bias. We develop a controlled framework that injects common learner errors into prompts and apply it to three bias benchmarks covering stereotype alignment and demographic preference across gender, race, and religion. We find that grammatical errors increase bias, and the effect grows with the error rate. Among the four com
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