Bridging Language Barriers: The Impact of Large Language Models on Academic Writing

Burak Dalaman (), Ali Furkan Kalay () and Nathan Kettlewell
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Burak Dalaman: University of London
Ali Furkan Kalay: Macquarie University, Sydney

No 18215, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Large language models (LLMs) have altered the nature of academic writing. While the influence of LLMs on academic writing is not uncontroversial, one promise for this technology is to bridge language barriers faced by nonnative English-speaking researchers. This study empirically demonstrates that LLMs have led to convergence in the lexical diversity of native and nonnative speakers, potentially helping to level the playing field. There has also been an increase in language complexity for nonnatives. We classify over one million authors as native or nonnative English speakers based on the etymological origins of their names and analyze over one million abstracts from arXiv.org, evaluating changes in lexical diversity and readability before and after ChatGPT’s release in November 2022. The results demonstrate a sharp increase in writing sophistication among all researchers, with nonnative English speakers showing the greatest gains across all writing metrics. Our findings provide empirical evidence on the impact of LLMs in academic writing, supporting recent speculations about their potential to bridge language barriers.

Keywords: technology adoption; large language models; academic equity; generative AI; language barrier; bayesian structural time series (search for similar items in EconPapers)
JEL-codes: I23 J24 (search for similar items in EconPapers)
Date: 2025-10
New Economics Papers: this item is included in nep-ain, nep-big, nep-inv and nep-lma
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