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Unequal Scientific Recognition in the Age of LLMs
Liu, Yixuan Elekes, Ábel Lu, Jianglin Dorantes-Gilardi, Rodrigo Barabási, Albert László
Liu, Yixuan
Elekes, Ábel
Lu, Jianglin
Dorantes-Gilardi, Rodrigo
Barabási, Albert László
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
Computational Theory and Mathematics
Computer Science Applications
Information Systems
Linguistics and Language
Computer Science Applications
Information Systems
Linguistics and Language
Files
URI
https://hdl.handle.net/20.500.14018/28820
Abstract
Large language models (LLMs) are reshaping how scientific knowledge is accessed and represented. This study evaluates the extent to which popular and frontier LLMs including GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro recognize scientists, benchmarking their outputs against OpenAlex and Wikipedia. Using a dataset focusing on 100,000 physicists from OpenAlex to evaluate LLM recognition, we uncover substantial disparities: LLMs exhibit selective and inconsistent recognition patterns. Recognition correlates strongly with scholarly impact such as citations, and remains uneven across gender and geography. Women researchers, and researchers from Africa, Asia, and Latin America are significantly underrecognized. We further examine the role of training data provenance, identifying Wikipedia as a potential sources that contributes to recognition gaps. Our findings highlight how LLMs can reflect, and potentially amplify existing disparities in science, underscoring the need for more transparent and inclusive knowledge systems.
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Publisher
Place of Publication
Type
Conference paper
Date
2025
Language
ISBN
9798891763357
Identifiers
10.18653/v1/2025.findings-emnlp.1279