Science out of its Ivory Tower: improving accessibility with reinforcement learning

Haining Wang (), Jason Clark (), Hannah McKelvey (), Leila Sterman (), Zheng Gao (), Zuoyu Tian (), Sandra Kübler () and Xiaozhong Liu ()
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Haining Wang: Indiana University
Jason Clark: Montana State University
Hannah McKelvey: Montana State University
Leila Sterman: Montana State University
Zheng Gao: Coupang
Zuoyu Tian: Macalester College
Sandra Kübler: Indiana University
Xiaozhong Liu: Worcester Polytechnic Institute

Scientometrics, 2025, vol. 130, issue 8, No 13, 4519-4543

Abstract: Abstract A vast amount of scholarly work is published daily, yet much of it remains inaccessible to the general public due to dense jargon and complex language. To address this challenge in science communication, we introduce a reinforcement learning framework that fine-tunes a language model to rewrite scholarly abstracts into more comprehensible versions. Guided by a carefully balanced combination of word- and sentence-level accessibility rewards, our language model effectively substitutes technical terms with more accessible alternatives, a task which models supervised fine-tuned or guided by conventional readability measures struggle to accomplish. Our best model adjusts the readability level of scholarly abstracts by approximately six U.S. grade levels—in other words, from a postgraduate to a high school level. This translates to roughly a 90% relative boost over the supervised fine-tuning baseline, all while maintaining factual accuracy and high-quality language. An in-depth analysis of our approach shows that balanced rewards lead to systematic modifications in the base model, likely contributing to smoother optimization and superior performance. We envision this work as a step toward bridging the gap between scholarly research and the general public, particularly younger readers and those without a college degree.

Keywords: Accessible language; Science communication; Language model; Text simplification; Reinforcement learning; Open science (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11192-025-05386-z

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