标题： 谁会被推荐？ 研究大型语言模型在论文推荐中的性别、种族和国家差异 显示英文标题

标题： Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models

摘要： 本文研究了几种代表性大模型在文献推荐任务中的表现，并探讨了研究曝光中的潜在偏差。 结果表明，不仅大型语言模型的整体推荐准确性仍然有限，而且这些模型倾向于推荐引用次数较多、发表日期较晚和作者团队较大的文献。 然而，在学者推荐任务中，没有证据表明大型语言模型不成比例地推荐男性、白人或发达国家的作者，这与已知的人类偏见模式形成对比。 显示英文摘要

摘要： This paper investigates the performance of several representative large models in the tasks of literature recommendation and explores potential biases in research exposure. The results indicate that not only LLMs' overall recommendation accuracy remains limited but also the models tend to recommend literature with greater citation counts, later publication date, and larger author teams. Yet, in scholar recommendation tasks, there is no evidence that LLMs disproportionately recommend male, white, or developed-country authors, contrasting with patterns of known human biases.