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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.

SourceHacker News AIAuthor: horseradish
EditLens: Quantifying the extent of AI editing in text
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Key points

  • EditLens uses lightweight similarity metrics validated by human annotators to measure AI editing magnitude.
  • The regression model achieves state-of-the-art F1 scores of 94.7% (binary) and 90.4% (ternary).
  • A case study applies the model to Grammarly, a popular writing assistant.
  • The research impacts authorship attribution, education, and policy, with public release of models and dataset.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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