ChatGPT as Linguistic Equalizer? Quantifying LLM-Driven Lexical Shifts in Academic Writing

Dingkang Lin, Naixuan Zhao, Dan Tian, Jiang Li
4/10/2025
cs.CL

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Abstract

The advent of ChatGPT has profoundly reshaped scientific research practices, particularly in academic writing, where non-native English-speakers (NNES) historically face linguistic barriers. This study investigates whether ChatGPT mitigates these barriers and fosters equity by analyzing lexical complexity shifts across 2.8 million articles from OpenAlex (2020-2024). Using the Measure of Textual Lexical Diversity (MTLD) to quantify vocabulary sophistication and a difference-in-differences (DID) design to identify causal effects, we demonstrate that ChatGPT significantly enhances lexical complexity in NNES-authored abstracts, even after controlling for article-level controls, authorship patterns, and venue norms. Notably, the impact is most pronounced in preprint papers, technology- and biology-related fields and lower-tier journals. These findings provide causal evidence that ChatGPT reduces linguistic disparities and promotes equity in global academia.

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Cite this paper

@article{lin2025chatgpt,
  title  = {ChatGPT as Linguistic Equalizer? Quantifying LLM-Driven Lexical Shifts in Academic Writing},
  author = {Dingkang Lin and Naixuan Zhao and Dan Tian and Jiang Li},
  year   = {2025},
  eprint = {2504.12317},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.12317v1}
}

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