Quantifying the Risks of Tool-assisted Rephrasing to Linguistic Diversity
This addresses a societal risk for users and content creators, but it is incremental as it builds on existing evaluations of individual tool effectiveness.
The paper tackled the problem of how writing assistants and large language models might reduce linguistic diversity when widely used, by measuring semantic and vocabulary changes from rephrasing tools on human-generated text.
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.