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    <abstract>Writing assistants and large language models see widespread use in the creation of text content. While their effectiveness for individual users has been evaluated in the literature, little is known about their proclivity to change language or reduce its richness when adopted by a large user base. In this paper, we take a first step towards quantifying this risk by measuring the semantic and vocabulary change enacted by the use of rephrasing tools on a multi-domain corpus of human-generated text.</abstract>
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%0 Conference Proceedings
%T Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity
%A Wang, Mengying
%A Spitz, Andreas
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F wang-spitz-2025-quantifying
%X Writing assistants and large language models see widespread use in the creation of text content. While their effectiveness for individual users has been evaluated in the literature, little is known about their proclivity to change language or reduce its richness when adopted by a large user base. In this paper, we take a first step towards quantifying this risk by measuring the semantic and vocabulary change enacted by the use of rephrasing tools on a multi-domain corpus of human-generated text.
%R 10.18653/v1/2025.findings-emnlp.1228
%U https://aclanthology.org/2025.findings-emnlp.1228/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1228
%P 22561-22574

Markdown (Informal)

[Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity](https://aclanthology.org/2025.findings-emnlp.1228/) (Wang & Spitz, Findings 2025)

• Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity (Wang & Spitz, Findings 2025)

ACL

• Mengying Wang and Andreas Spitz. 2025. Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22561–22574, Suzhou, China. Association for Computational Linguistics.