← 研踪 PaperTrailarXiv:2611.13024 · v1
GRAP: Gender Rubric-Aware Probing for Analyzing Gender Bias in LLMs
首次提交 2026-11-09 16:09
版本更新 2026-11-09 16:09
雷达观测 2026-11-11 11:05
摘要
As LLMs increasingly mediate everyday interactions, subtle gender biases in open-ended responses pose growing societal risks. Existing probing methods have two key limitations: they rely on fixed-format tasks, such as pronoun resolution or occupation association, that alignment-tuned models can easily handle; and they compare raw group-level rubric scores, ignoring per-dimension variance and obscuring which bias dimensions reliably distinguish gender groups. We introduce GRAP (Gender Rubric-Aware Probing), a framework that uses a lightweight logistic probe to identify the most discriminative bias dimensions from responses scored across 10 rubric dimensions, operating task-agnostically on open-ended instruction following.
作者备注：3 pages. Accepted at the ECML-PKDD 2026 Workshop on Bias and Fairness in AI
观测到的版本
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v1 · 2026-11-09 16:09 · 最新观测版本
GRAP: Gender Rubric-Aware Probing for Analyzing Gender Bias in LLMs
As LLMs increasingly mediate everyday interactions, subtle gender biases in open-ended responses pose growing societal risks. Existing probing methods have two key limitations: they rely on fixed-format tasks, such as pronoun resolution or occupation association, that alignment-tuned models can easily handle; and they compare raw group-level rubric scores, ignoring per-dimension variance and obscuring which bias dimensions reliably distinguish gender groups. We introduce GRAP (Gender Rubric-Aware Probing), a framework that uses a lightweight logistic probe to identify the most discriminative bias dimensions from responses scored across 10 rubric dimensions, operating task-agnostically on open-ended instruction following.
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