Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations

TL;DR

This study evaluates six open-weight LLMs in recommending physics scholars, revealing biases, inconsistencies, and limitations in accuracy, diversity, and representation, emphasizing the need for improved scholarly recommendation models.

Contribution

It provides a comprehensive benchmarking of LLMs for scholar recommendation, highlighting biases, variability, and areas for enhancement in accuracy and diversity.

Findings

Mixtral-8x7b offers the most stable outputs.

Llama3.1-70b shows the highest variability.

Models tend to favor senior, highly cited scholars.

Abstract

This paper evaluates the performance of six open-weight LLMs (llama3-8b, llama3.1-8b, gemma2-9b, mixtral-8x7b, llama3-70b, llama3.1-70b) in recommending experts in physics across five tasks: top-k experts by field, influential scientists by discipline, epoch, seniority, and scholar counterparts. The evaluation examines consistency, factuality, and biases related to gender, ethnicity, academic popularity, and scholar similarity. Using ground-truth data from the American Physical Society and OpenAlex, we establish scholarly benchmarks by comparing model outputs to real-world academic records. Our analysis reveals inconsistencies and biases across all models. mixtral-8x7b produces the most stable outputs, while llama3.1-70b shows the highest variability. Many models exhibit duplication, and some, particularly gemma2-9b and llama3.1-8b, struggle with formatting errors. LLMs generally recommend real scientists, but accuracy drops in field-, epoch-, and seniority-specific queries, consistently favoring senior scholars. Representation biases persist, replicating gender imbalances (reflecting male predominance), under-representing Asian scientists, and over-representing White scholars. Despite some diversity in institutional and collaboration networks, models favor highly cited and productive scholars, reinforcing the rich-getricher effect while offering limited geographical representation. These findings highlight the need to improve LLMs for more reliable and equitable scholarly recommendations.