EditLens: Quantifying the extent of AI editing in text
Researchers propose EditLens, a method using lightweight similarity metrics and a regression model to quantify the amount of AI editing in text. It achieves state-of-the-art performance on binary (F1=94.7%) and ternary (F1=90.4%) classification distinguishing human, AI, and mixed writing. A case study analyzes Grammarly's AI edits. The work has implications for authorship attribution, education, and policy, and the team commits to releasing models and data.
[2510.03154] EditLens: Quantifying the Extent of AI Editing in Text
[Submitted on 3 Oct 2025]
Title:EditLens: Quantifying the Extent of AI Editing in Text
View a PDF of the paper titled EditLens: Quantifying the Extent of AI Editing in Text, by Katherine Thai and 3 other authors
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Abstract:A significant proportion of queries to large language models ask them to edit user-provided text, rather than generate new text from scratch. While previous work focuses on detecting fully AI-generated text, we demonstrate that AI-edited text is distinguishable from human-written and AI-generated text. First, we propose using lightweight similarity metrics to quantify the magnitude of AI editing present in a text given the original human-written text and validate these metrics with human annotators. Using these similarity metrics as intermediate supervision, we then train EditLens, a regression model that predicts the amount of AI editing present within a text. Our model achieves state-of-the-art performance on both binary (F1=94.7%) and ternary (F1=90.4%) classification tasks in distinguishing human, AI, and mixed writing. Not only do we show that AI-edited text can be detected, but also that the degree of change made by AI to human writing can be detected, which has implications for authorship attribution, education, and policy. Finally, as a case study, we use our model to analyze the effects of AI-edits applied by Grammarly, a popular writing assistance tool. To encourage further research, we commit to publicly releasing our models and dataset.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2510.03154 [cs.CL]
(or arXiv:2510.03154v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2510.03154
arXiv-issued DOI via DataCite
Submission history
From: Katherine Thai [view email] [v1] Fri, 3 Oct 2025 16:27:48 UTC (5,596 KB)
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