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        <title>Human-LLM Coevolution: Evidence from Academic Writing</title>
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            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>With a statistical analysis of arXiv paper abstracts, we report a marked drop in the frequency of several words previously identified as overused by ChatGPT, such as “delve”, starting soon after they were pointed out in early 2024. The frequency of certain other words favored by ChatGPT, such as “significant”, has instead kept increasing. These phenomena suggest that some authors of academic papers have adapted their use of large language models (LLMs), for example, by selecting outputs or applying modifications to the LLM-generated content. Such coevolution and cooperation of humans and LLMs thus introduce additional challenges to the detection of machine-generated text in real-world scenarios. Estimating the impact of LLMs on academic writing by examining word frequency remains feasible, and more attention should be paid to words that were already frequently employed, including those that have decreased in frequency due to LLMs’ disfavor. The coevolution between humans and LLMs also merits further study.</abstract>
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    <identifier type="doi">10.18653/v1/2025.findings-acl.657</identifier>
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%0 Conference Proceedings
%T Human-LLM Coevolution: Evidence from Academic Writing
%A Geng, Mingmeng
%A Trotta, Roberto
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F geng-trotta-2025-human
%X With a statistical analysis of arXiv paper abstracts, we report a marked drop in the frequency of several words previously identified as overused by ChatGPT, such as “delve”, starting soon after they were pointed out in early 2024. The frequency of certain other words favored by ChatGPT, such as “significant”, has instead kept increasing. These phenomena suggest that some authors of academic papers have adapted their use of large language models (LLMs), for example, by selecting outputs or applying modifications to the LLM-generated content. Such coevolution and cooperation of humans and LLMs thus introduce additional challenges to the detection of machine-generated text in real-world scenarios. Estimating the impact of LLMs on academic writing by examining word frequency remains feasible, and more attention should be paid to words that were already frequently employed, including those that have decreased in frequency due to LLMs’ disfavor. The coevolution between humans and LLMs also merits further study.
%R 10.18653/v1/2025.findings-acl.657
%U https://aclanthology.org/2025.findings-acl.657/
%U https://doi.org/10.18653/v1/2025.findings-acl.657
%P 12689-12696

Markdown (Informal)

[Human-LLM Coevolution: Evidence from Academic Writing](https://aclanthology.org/2025.findings-acl.657/) (Geng & Trotta, Findings 2025)

• Human-LLM Coevolution: Evidence from Academic Writing (Geng & Trotta, Findings 2025)

ACL

• Mingmeng Geng and Roberto Trotta. 2025. Human-LLM Coevolution: Evidence from Academic Writing. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12689–12696, Vienna, Austria. Association for Computational Linguistics.