谁被引用？LLM驱动的参考文献选择中的性别与多数群体偏见
Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection
AI总结：
本研究通过受控实验发现，LLM在参考文献选择中表现出对男性作者和多数性别群体的双重偏见，且偏见随候选池增大而加剧，提示需在学术工作流中引入缓解策略。
摘要：
大型语言模型（LLMs）正迅速被用作研究助手，特别是在文献综述和参考文献推荐方面，然而关于它们是否会将人口统计学偏见引入引用工作流程，目前知之甚少。本研究通过使用化名作者姓名的受控实验，系统性地调查了LLM驱动的参考文献选择中的性别偏见。我们通过改变候选参考文献池中的性别构成，并分析跨领域的引用选择模式，评估了多种LLM（GPT-4o、GPT-4o-mini、Claude Sonnet和Claude Haiku）。我们的结果揭示了两种形式的偏见：一种是对男性作者参考文献的持续偏好，另一种是多数群体偏见，即偏向于候选池中占多数的性别。这些偏见在更大的候选池中被放大，而基于提示的缓解策略只能适度减弱它们。领域层面的分析表明，偏见程度在不同科学领域间有所差异，其中社会科学表现出的偏见最小。我们的发现表明，LLM可能会强化或加剧学术认可中现有的性别失衡。在将LLM整合到高风险的学术工作流程之前，需要有效的缓解策略来避免延续科学引用实践中现有的性别差异。
英文摘要：
Large language models (LLMs) are rapidly being adopted as research assistants, particularly for literature review and reference recommendation, yet little is known about whether they introduce demographic bias into citation workflows. This study systematically investigates gender bias in LLM-driven reference selection using controlled experiments with pseudonymous author names. We evaluate several LLMs (GPT-4o, GPT-4o-mini, Claude Sonnet, and Claude Haiku) by varying gender composition within candidate reference pools and analyzing selection patterns across fields. Our results reveal two forms of bias: a persistent preference for male-authored references and a majority-group bias that favors whichever gender is more prevalent in the candidate pool. These biases are amplified in larger candidate pools and only modestly attenuated by prompt-based mitigation strategies. Field-level analysis indicates that bias magnitude varies across scientific domains, with social sciences showing the least bias. Our findings indicate that LLMs can reinforce or exacerbate existing gender imbalances in scholarly recognition. Effective mitigation strategies are needed to avoid perpetuating existing gender disparities in scientific citation practices before integrating LLMs into high-stakes academic workflows.
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