# 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions

arXiv: https://arxiv.org/abs/2606.04906 · HTML: https://mlsanity.com/paper/2606.04906/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 25 of 25)

- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [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)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://mlsanity.com/paper/2405.07940/) (2024)
- [MAGE: Machine-generated Text Detection in the Wild](https://mlsanity.com/paper/2305.13242/) (2023)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text](https://mlsanity.com/paper/2212.12672/) (2022)
- [LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?](https://mlsanity.com/paper/2401.05952/) (2024)
- [CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts](https://mlsanity.com/paper/2304.12008/) (2023)
- [Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review](https://mlsanity.com/paper/2204.10805/) (2022)
- [Technical Report on the Pangram AI-Generated Text Classifier](https://mlsanity.com/paper/2402.14873/) (2024)
- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts](https://mlsanity.com/paper/2311.08374/) (2023)
- [Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights](https://mlsanity.com/paper/2403.03506/) (2024)
- [Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing](https://mlsanity.com/paper/2502.15666/) (2025)
- [Membership Inference Attacks on Large-Scale Models: A Survey](https://mlsanity.com/paper/2503.19338/) (2025)
- [Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education](https://mlsanity.com/paper/2307.12267/) (2023)
- [Machine-generated text detection prevents language model collapse](https://mlsanity.com/paper/2502.15654/) (2025)
- [Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines](https://mlsanity.com/paper/2312.05235/) (2023)
- [CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection](https://mlsanity.com/paper/2510.23845/) (2025)
- [CARE: Collaborative AI-Assisted Reading Environment](https://mlsanity.com/paper/2302.12611/) (2023)

## Built on by (most cited first, 3 of 3)

- [Pangram 4 Technical Report](https://mlsanity.com/paper/2607.27183/) (2026)
- [ARB: A Matched Authorship-Rewriting Benchmark Dataset for AI-Text Detector Evaluation](https://mlsanity.com/paper/2607.29539/) (2026)
- [Large Language Model Turnover Undermines Screening for Artificial Intelligence-Assisted Scientific Writing](https://mlsanity.com/paper/2610.11599/) (2026)

## Abstract (arXiv)

Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use. Rather, existing datasets and approaches often define their own criteria and make their own assumptions, sometimes implicitly, and often only loosely related to real-world needs and applications. To address this gap, we here systematically define various notions of AI-generated text and their characteristics. To study these, we collect AITDNA - a new benchmark of human-machine co-constructed texts that is annotated with detailed genesis information, such as the entire edit and AI-interaction history. We benchmark various machine-generated text detectors and find that they often only perform well for specific notions but not as broad detectors. We release code and data publicly.