It’s How You Ask: Gender-Associated Linguistic Bias in LLMs

Professional communication is increasingly mediated by LLMs — but do these models serve all users equally? We show that female-associated linguistic features in prompts (hedges, tag questions, collective reference) systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models. These effects persist after controlling for prompt complexity and feature carry-over. Explicit gender cues like sign-off names are encoded in the same representational space as linguistic dialect — suggesting shared underlying mechanisms — yet linguistic register is far more influential, producing large, consistent effects where names produce none. Our results further reveal that post-hoc mitigation is challenging: because these patterns are culturally embedded and outside conscious control, users cannot avoid them through strategic self-presentation, and mechanistic analysis reveals that linguistic features are encoded in early transformer layers and entangled with other features. However, intervention is difficult. Our work calls for upstream consideration of the influences of linguistic variation to mitigate disparate impacts of LLM-mediated workplace communication.