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 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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: field”
- LinkedLinked via arxiv author · 85%Katherine Van Koevering →
“It's How You Ask: Gender-Associated Linguistic Bias in LLMs”
- LinkedLinked via arxiv author · 85%Anjalie Field →
“It's How You Ask: Gender-Associated Linguistic Bias in LLMs”
- PossiblePossibly related (embedding) · 63%It's How You Ask: Gender-Associated Linguistic Bias in LLMs →
