The paper characterizes deductive stereotyping in LLMs and introduces Fair-GCG to discover injection phrases that improve fairness across benchmarks, reasoning, and real-world tasks.
Felkner, Ho-Chun Herbert Chang, Eugene Jang, and Jonathan May
4 Pith papers cite this work. Polarity classification is still indexing.
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LLMs exhibit misfired alignment on stereotype questions at 4.7-18.9% rates on the new VETO benchmark of 2,032 contrastive pairs, unlike humans at 0%, due to overgeneralized safety cues after instruction tuning.
One-shot GRPO on a single biased example induces generalizing stereotype bias in post-trained LLMs, with susceptibility varying by initial bias likelihood.
The paper introduces a taxonomy of AI safety for LLMs organized into Trustworthy AI, Responsible AI, and Safe AI perspectives, accompanied by a review of state-of-the-art methods, challenges, and future directions.
citing papers explorer
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Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG
The paper characterizes deductive stereotyping in LLMs and introduces Fair-GCG to discover injection phrases that improve fairness across benchmarks, reasoning, and real-world tasks.
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The Wrong Kind of Right: Quantifying and Localizing Misfired Alignment in LLMs
LLMs exhibit misfired alignment on stereotype questions at 4.7-18.9% rates on the new VETO benchmark of 2,032 contrastive pairs, unlike humans at 0%, due to overgeneralized safety cues after instruction tuning.
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It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO
One-shot GRPO on a single biased example induces generalizing stereotype bias in post-trained LLMs, with susceptibility varying by initial bias likelihood.