Awesome LLM-generated Text Detection

The powerful ability of large language models (LLMs) to understand, follow, and generate complex languages has enabled LLM-generated texts to flood many areas of our daily lives at an incredible rate, with potentially negative impacts and risks on society and academia. As LLMs continue to expand, how can we detect LLM-generated texts to help minimize the threat posed by the misuse of LLMs?

¹ Junchao Wu, ¹ Shu Yang, ¹ Runzhe Zhan, ¹ ² Yulin Yuan, ¹ Derek Fai Wong, ¹ Lidia Sam Chao

¹ University of Macau, ² Peking University

📢 News

• [2026.05] ✨ Our paper is accepted by ACL 2026. We released DetectRL-X, a comprehensive multilingual benchmark for LLM-generated text detection, covering 8 languages, 6 domains, 4 commercial LLMs, and 8 evaluation dimensions. Please refer to arXiv: DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection and Github Repo DetectRL-X for details.
• [2025.12] ✨ Our paper is published in TACL. We released RepreGuard, an efficient statistics-based detection method that leverages hidden representation patterns of LLMs to distinguish between LLM-generated and human-written texts. Please refer to arXiv: RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns and Github Repo RepreGuard for details.
• [2025.03] ✨ Our survey paper is accepted by Computational Linguistics (Volume 51, Issue 1). Please refer to A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions for details.
• [2024.11.28] ✨ Our paper is accepted by COLING 2025. We released GECScore, a simple yet effective black-box zero-shot detection approach based on the observation that human-written texts typically contain more grammatical errors than LLM-generated texts. Please refer to arXiv: Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore and Github Repo GECScore for details.
• [2024.09.26] ✨ Our benchmark paper is accepted by NeurIPS 2024 D&B track. We released DetectRL, a benchmark for real-world LLM-generated text detection, provide real utility to researchers on the topic and practitioners looking for consistent evaluation methods. Please refer to arXiv: DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios and Github Repo DetectRL for details.
• [2023.10.24] Our survey paper is now available on arXiv: A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions.
• [2023.05.01] : We began to explore the topic of LLM-generated Text Detection.

🔍 Table of Contents

• Awesome LLM-generated Text Detection
  • 📢 News
  • � Table of Contents
  • 📃 Papers
    • Overview
    • Datasets
      • Benchmarks
      • Potential Datasets
    • Detector
      • Watermarking Technology
      • Statistics-based Detectors
      • Neural-based Detectors
      • Human-assisted Methods
      • Detector Attack
      • Shared Task
    • Related Works
      • Other Surveys
  • 🚩 Citation
  • Contributing

📃 Papers

Overview

A survey and reflection on the latest research breakthroughs in LLM-generated Text detection, including data, detectors, metrics, current issues and future directions. Please refer to our article/paper for more details.

Datasets

Benchmarks

Potential Datasets

Detectors

Watermarking Technology

Statistics-based Detectors

Neural-based Detectors

Human-assisted Methods

Detector Attack

Shared Task

Related Works

Other Surveys

🚩 Citation

If our research helps you, please kindly cite our paper.

@article{wu2025survey,
      title={A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions}, 
      author={Junchao Wu and Shu Yang and Runzhe Zhan and Yulin Yuan and Lidia Sam Chao and Derek Fai Wong},
      journal      = {Computational Linguistics},
      volume       = {51},
      number       = {1},
      year         = {2025},
      pages        = {275--338},
      url          = {https://aclanthology.org/2025.cl-1.8/},
}

@inproceedings{wu2025GECScore,
      title={Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore}, 
      author={Junchao Wu and Runzhe Zhan and Derek F. Wong and Shu Yang and Xuebo Liu and Lidia S. Chao and Min Zhang},
      booktitle    = {Proceedings of the 31st International Conference on Computational Linguistics},
      year         = {2025},
      pages        = {10275--10292},
      url          = {https://aclanthology.org/2025.coling-main.684/},
}

@article{chen2025RepreGuard,
      title={RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns}, 
      author={Xin Chen and Junchao Wu and Shu Yang and Runzhe Zhan and Zeyu Wu and Ziyang Luo and Di Wang and Min Yang and Lidia S. Chao and Derek F. Wong},
      journal      = {Transactions of the Association for Computational Linguistics},
      volume       = {13},
      year         = {2025},
      pages        = {1812--1831},
      url          = {https://aclanthology.org/2025.tacl-1.81/},
}

@inproceedings{wu2026DetectRLX,
      title={DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection}, 
      author={Junchao Wu and Yefeng Liu and Chenyu Zhu and Hao Zhang and Zeyu Wu and Tianqi Shi and Yichao Du and Longyue Wang and Weihua Luo and Jinsong Su and Derek F. Wong},
      booktitle    = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
      year         = {2026},
      pages        = {38247--38294},
      url          = {https://aclanthology.org/2026.acl-long.1773/},
}

@inproceedings{wu2024DetectRL,
      title={DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios}, 
      author={Junchao Wu and Runzhe Zhan and Derek F. Wong and Shu Yang and Xinyi Yang and Yulin Yuan and Lidia S. Chao},
      booktitle    = {Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track},
      year         = {2024},
      url          = {https://proceedings.neurips.cc/paper_files/paper/2024/hash/b61bdf7e9f64c04ec75a26e781e2ad51-Abstract-Datasets_and_Benchmarks_Track.html},
}

Contributing

Contributions are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request. We appreciate your contributions to making LLM-generated Text Detection work even better.