# EditLens: Quantifying the Extent of AI Editing in Text

arXiv: https://arxiv.org/abs/2510.03154 · HTML: https://mlsanity.com/paper/2510.03154/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 28 of 28)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text](https://mlsanity.com/paper/2305.17359/) (2023)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [Better Summarization Evaluation with Word Embeddings for ROUGE](https://mlsanity.com/paper/1508.06034/) (2015)
- [SeqXGPT: Sentence-Level AI-Generated Text Detection](https://mlsanity.com/paper/2310.08903/) (2023)
- [Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits](https://mlsanity.com/paper/2409.14509/) (2024)
- [People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text](https://mlsanity.com/paper/2501.15654/) (2025)
- [Technical Report on the Pangram AI-Generated Text Classifier](https://mlsanity.com/paper/2402.14873/) (2024)
- [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://mlsanity.com/paper/2202.13469/) (2022)
- [Beemo: Benchmark of Expert-edited Machine-generated Outputs](https://mlsanity.com/paper/2411.04032/) (2024)
- [LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection](https://mlsanity.com/paper/2408.04284/) (2024)
- [AI-generated text boundary detection with RoFT](https://mlsanity.com/paper/2311.08349/) (2023)
- [Linq-Embed-Mistral Technical Report](https://mlsanity.com/paper/2412.03223/) (2024)
- [DAMAGE: Detecting Adversarially Modified AI Generated Text](https://mlsanity.com/paper/2501.03437/) (2025)
- [Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing](https://mlsanity.com/paper/2502.15666/) (2025)
- [Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text](https://mlsanity.com/paper/2506.07001/) (2025)
- [HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoring](https://mlsanity.com/paper/2506.02959/) (2025)
- [Real, Fake, or Manipulated? Detecting Machine-Influenced Text](https://mlsanity.com/paper/2509.15350/) (2025)

## Built on by (most cited first, 26 of 26)

- [Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?](https://mlsanity.com/paper/2602.11988/) (2026)
- [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026)
- [AI use in American newspapers is widespread, uneven, and rarely disclosed](https://mlsanity.com/paper/2510.18774/) (2025)
- [StoryScope: Investigating idiosyncrasies in AI fiction](https://mlsanity.com/paper/2604.03136/) (2026)
- [LLM-Generated or Human-Written? Comparing Review and Non-Review Papers on ArXiv](https://mlsanity.com/paper/2601.17036/) (2026)
- [Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review](https://mlsanity.com/paper/2601.20920/) (2026)
- [Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews](https://mlsanity.com/paper/2605.21713/) (2026)
- [Pangram 4 Technical Report](https://mlsanity.com/paper/2607.27183/) (2026)
- [Base Models Look Human To AI Detectors](https://mlsanity.com/paper/2605.19516/) (2026)
- [AI Text Detectors and the Misclassification of Slightly Polished Arabic Text](https://mlsanity.com/paper/2511.16690/) (2025)
- [Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection](https://mlsanity.com/paper/2606.06481/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [EVIL-Detect for NLPCC 2026 Shared Task 6: LLM-Generated Text Detection](https://mlsanity.com/paper/2608.10698/) (2026)
- [Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments](https://mlsanity.com/paper/2601.11093/) (2026)
- [Show, Don't TELL: Explainable AI-Generated Text Detection](https://mlsanity.com/paper/2605.27921/) (2026)
- [Non-Parametric Machine Text Detection via Multi-View Gaussian Processes](https://mlsanity.com/paper/2606.14060/) (2026)
- [Linear Probing Provides Robust and Efficient Detection of Machine-Generated Text](https://mlsanity.com/paper/2608.24780/) (2026)
- [AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not](https://mlsanity.com/paper/2608.27855/) (2026)
- [Value Over Language Model: Detecting Original Contribution in Writing](https://mlsanity.com/paper/2609.00700/) (2026)
- [MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts](https://mlsanity.com/paper/2609.02379/) (2026)
- [How Much Were You Told? Measuring External Information in Peer Reviews](https://mlsanity.com/paper/2609.28041/) (2026)
- [Agents Can Use Base Models to Evade AI Detection](https://mlsanity.com/paper/2609.31876/) (2026)
- [MixDetect: Word-Level Localization and Quantification of AI Editing](https://mlsanity.com/paper/2609.32625/) (2026)
- [LA-CPD: Local-Evidence-Aware Change-Point Detection for Human-LLM Authorship Segmentation](https://mlsanity.com/paper/2609.33787/) (2026)
- [Policy-Conditioned AI-Use Detection: An Evidentiary Framework for Academic Publishing](https://mlsanity.com/paper/2609.38427/) (2026)
- [How Much Is an AI Token Worth? Scaling Laws for Wild AI-Generated Web Text](https://mlsanity.com/paper/2609.40295/) (2026)

## Abstract (arXiv)

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.