[2608.13328] It's How You Ask: Gender-Associated Linguistic Bias in LLMs

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arXiv:2608.13328 (cs)

[Submitted on 13 Aug 2026]

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

Authors:Katherine Van Koevering, Anjalie Field<br>View a PDF of the paper titled It's How You Ask: Gender-Associated Linguistic Bias in LLMs, by Katherine Van Koevering and Anjalie Field

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Abstract:Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they 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 easily avoid them through strategic self-presentation, and mechanistic analysis reveals that linguistic features are encoded in early transformer layers and entangled with other features. Our work calls for upstream consideration of the influences of linguistic variation to mitigate disparate impacts of LLM-mediated workplace communication.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2608.13328 [cs.CL]

(or<br>arXiv:2608.13328v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.13328

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Katherine Van Koevering [view email]<br>[v1]<br>Thu, 13 Aug 2026 14:54:23 UTC (84 KB)

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