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Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing -- differences in how semantically equivalent prompts are expressed (e.g., \"A is better than B\" vs. \"B is worse than A\") -- as an underexplored contributor to this gap. We first introduce the concept of \"framing disparity\" to quantify the impact of framing on fairness evaluation. By augmenting fairness evaluation benchmarks with alternative framings, we find that (1) fairness scores vary significantly with framing and (2) existing debiasing methods improve overall (i.e., frame-averaged) fairness, but often fail to reduce framing-induced disparities. To address this, we pr","title":"DeFrame: Debiasing Large Language Models Against Framing Effects","url":"https://arxiv.org/abs/2602.04306","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.04306v2 Announce Type: replace-cross \nAbstract: As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing -- differences in how semantically equivalent prompts are expressed (e.g., \"A is better than B\" vs. \"B is worse than A\") -- as an underexplored contributor to this gap. We first introduce the concept of \"framing disparity\" to quantify the impact of framing on fairness evaluation. By augmenting fairness evaluation benchmarks with alternative framings, we find that (1) fairness scores vary significantly with framing and (2) existing debiasing methods improve overall (i.e., frame-averaged) fairness, but often fail to reduce framing-induced disparities. 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