分析大模型论文推荐中的性别、种族和国家偏见，发现其推荐更倾向高被引、新发表和团队大的论文。
Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models
- 对比多款大模型在文献推荐中的表现，评估其推荐策略。
- 模型偏好高被引、近年发表和作者团队大的论文，但无明显性别/种族/国家偏见。
- 适合关注AI推荐系统公平性与学术传播不平等的研究者阅读。
本文研究了几种代表性大模型在文献推荐任务中的表现，并探讨了科研曝光中可能存在的偏见。结果显示，尽管大模型的整体推荐准确率仍有限，但它们倾向于推荐被引次数更高、发表时间更晚以及作者团队更大的论文。然而，在学者推荐任务中，未发现大模型显著偏向男性、白人或来自发达国家的作者，这与已知的人类偏见模式形成对比。
原文摘要 · Abstract (English)
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.
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