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More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
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本文提出评估LLMs性别偏见的新框架,发现LLMs在职业角色中过度代表女性,并探讨其与人类刻板印象和现实数据的关系,强调平衡缓解措施的重要性。

arXiv:2503.15904v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have revolutionized natural language processing, yet concerns persist regarding their tendency to reflect or amplify social biases. This study introduces a novel evaluation framework to uncover gender biases in LLMs: using free-form storytelling to surface biases embedded within the models. A systematic analysis of ten prominent LLMs shows a consistent pattern of overrepresenting female characters across occupations, likely due to supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Paradoxically, despite this overrepresentation, the occupational gender distributions produced by these LLMs align more closely with human stereotypes than with real-world labor data. This highlights the challenge and importance of implementing balanced mitigation measures to promote fairness and prevent the establishment of potentially new biases. We release the prompts and LLM-generated stories at GitHub.

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LLMs 性别偏见 评估框架 职业角色 平衡缓解
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