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

arXiv: https://arxiv.org/abs/2501.00367 · HTML: https://mlsanity.com/paper/2501.00367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Scientific production in the era of Large Language Models](https://mlsanity.com/paper/2601.13187/) (2026)
- [Structurally Human, Semantically Biased: Detecting LLM-Generated References with Embeddings and GNNs](https://mlsanity.com/paper/2601.20704/) (2026)

## Abstract (arXiv)

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.