GRAP: Gender Rubric-Aware Probing for Analyzing Gender Bias in LLMs

arXiv:2611.13024v1 Announce Type: new Abstract: 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 logi

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