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Katherine Van Koevering

COLM 2026NeurIPS 2026可解释性多语言与语言学社会与人本智能体与工具
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1 一作 · 2 篇 · COLM 2026 / NeurIPS 2026

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2026 顶会论文

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一作COLM 2026Poster· Poster Session 3· Imperial Ballroom· #42

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

Katherine Van Koevering · Anjalie Field

A controlled experiment showing gender-associated linguistic features significantly impacts the sophistication of generated text

摘要

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.

可解释性多语言与语言学社会与人本large langauge modelsbiasgendergenerated textOpenReview

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