谁得到了推荐？探究大语言模型论文推荐中的性别、种族与国家差异
Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models
AI总结：
本文研究大语言模型在文献推荐中的表现与偏差，发现其准确率有限且偏好高引用、新近发表、大团队文献，但未表现出推荐男性、白人或发达国家作者的偏差。
摘要：
本文考察了若干代表性大模型在文献推荐任务中的表现，并探究研究曝光中可能存在的偏差。结果表明，不仅大语言模型的整体推荐准确率仍然有限，而且这些模型倾向于推荐引用次数更多、发表日期更晚、作者团队规模更大的文献。然而，在学者推荐任务中，没有证据表明大语言模型不成比例地推荐男性、白人或发达国家作者，这与已知的人类偏差模式形成对比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
