EditLens: Quantifying the Extent of AI Editing in Text

• URL: http://arxiv.org/abs/2510.03154v1
• Date: Fri, 03 Oct 2025 16:27:48 GMT
• Title: EditLens: Quantifying the Extent of AI Editing in Text
• Authors: Katherine Thai, Bradley Emi, Elyas Masrour, Mohit Iyyer,
• Abstract summary: We show that AI-edited text is distinguishable from human-written and AI-generated text.<n>We train a regression model that predicts the amount of AI editing present within a text.<n>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.
• Score: 23.457378805409714
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation [3.088244520495001]
  A sentence-level sequence labeling model proposed to detect transitions between human- and AI-generated text.<n>Our model combines the state-of-the-art pre-trained Transformer models, incorporating Neural Networks (NN) and Conditional Random Fields (CRFs)<n>The evaluation is performed on two publicly available benchmark datasets containing collaborative human and AI-generated texts.
  arXiv  Detail & Related papers  (2025-09-22T14:22:55Z)
• Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing [55.2480439325792]
  This study systematically evaluations twelve state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation dataset.<n>Our findings reveal that detectors frequently flag even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models.
  arXiv  Detail & Related papers  (2025-02-21T18:45:37Z)
• Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection [58.419940585826744]
  We introduce FairOPT, an algorithm for group-specific threshold optimization for probabilistic AI-text detectors.<n>We partitioned data into subgroups based on attributes (e.g., text length and writing style) and implemented FairOPT to learn decision thresholds for each group to reduce discrepancy.<n>Our framework paves the way for more robust classification in AI-generated content detection via post-processing.
  arXiv  Detail & Related papers  (2025-02-06T21:58:48Z)
• DAMAGE: Detecting Adversarially Modified AI Generated Text [0.13108652488669736]
  We show that many existing AI detectors fail to detect humanized text.<n>We demonstrate a robust model that can detect humanized AI text while maintaining a low false positive rate.
  arXiv  Detail & Related papers  (2025-01-06T23:43:49Z)
• Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text [4.902089836908786]
  WhosAI is a triplet-network contrastive learning framework designed to predict whether a given input text has been generated by humans or AI.<n>We show that our proposed framework achieves outstanding results in both the Turing Test and Authorship tasks.
  arXiv  Detail & Related papers  (2024-07-12T15:44:56Z)
• Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text [61.22649031769564]
  We propose a novel framework, paraphrased text span detection (PTD) PTD aims to identify paraphrased text spans within a text. We construct a dedicated dataset, PASTED, for paraphrased text span detection.
  arXiv  Detail & Related papers  (2024-05-21T11:22:27Z)
• Raidar: geneRative AI Detection viA Rewriting [42.477151044325595]
  Large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.
  arXiv  Detail & Related papers  (2024-01-23T18:57:53Z)
• On the Possibilities of AI-Generated Text Detection [76.55825911221434]
  We argue that as machine-generated text approximates human-like quality, the sample size needed for detection bounds increases. We test various state-of-the-art text generators, including GPT-2, GPT-3.5-Turbo, Llama, Llama-2-13B-Chat-HF, and Llama-2-70B-Chat-HF, against detectors, including oBERTa-Large/Base-Detector, GPTZero.
  arXiv  Detail & Related papers  (2023-04-10T17:47:39Z)
• Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense [56.077252790310176]
  We present a paraphrase generation model (DIPPER) that can paraphrase paragraphs, condition on surrounding context, and control lexical diversity and content reordering. Using DIPPER to paraphrase text generated by three large language models (including GPT3.5-davinci-003) successfully evades several detectors, including watermarking. We introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider.
  arXiv  Detail & Related papers  (2023-03-23T16:29:27Z)
• Can AI-Generated Text be Reliably Detected? [50.95804851595018]
  Large Language Models (LLMs) perform impressively well in various applications.<n>The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use.<n>We stress-test the robustness of these AI text detectors in the presence of an attacker.
  arXiv  Detail & Related papers  (2023-03-17T17:53:19Z)
• Text Editing by Command [82.50904226312451]
  A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations.
  arXiv  Detail & Related papers  (2020-10-24T08:00:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.