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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?
[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.
[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.
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.
On the Reliability of Watermarks for Large Language Models
ICLR 2024
2023-06
A Private Watermark for Large Language Models
ICLR 2024
2023-07
Distillation-Resistant Watermarking for Model Protection in NLP
arXiv
2022-10
Watermarking Pre-trained Language Models with Backdooring
arXiv
2022-10
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
NeurIPS 2022
2022-09
An Adaptive Watermark for Large Language Models
ICML 2024
2024-01
WaterBench: A Benchmark for LLM Watermarking
ACL 2024
2023-11
Watermarking Makes Language Models Radioactive
arXiv
2024-02
SynthID-Text: Identifying AI-Generated Text Content
Nature 2024
2024-10
Statistics-based Detectors
Paper
Venue
Date
Link
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
ICML 2023
2023-01
Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature
ICLR 2024
2023-10
Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
arXiv
2023-05
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text
arXiv
2023-05
GLTR: Statistical Detection and Visualization of Generated Text
ACL 2019 Demo
2019-06
HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis
arXiv
2023-05
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
arXiv
2023-06
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
COLING 2025
2024-05
RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
TACL 2025
2025-08
Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Text
arXiv
2023-07
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
ICLR 2024
2023-05
Binoculars: Zero-Shot Detection of Machine-Generated Text
ICML 2024
2024-01
LLMDet: A Third Party Large Language Models Generated Text Detection Tool
EMNLP 2023 Findings
2023-05
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
arXiv
2023-05
GPT-who: An Information Density-based Machine-Generated Text Detector
arXiv
2023-10
BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens
NeurIPS 2024
2024-06
Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling (POGER)
IJCAI 2024
2024-02
AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees
NeurIPS 2025
2025-10
Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection
ACL 2026
2026-03
Segmenting Human–LLM Co-authored Text via Change Point Detection
arXiv
2026-05
Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
TACL 2026
2026-06
Detecting LLM-Generated Tokens in Human–LLM Coauthored Text
arXiv
2026-07
Detecting LLM-Generated Text with Performance Guarantees
arXiv
2026-01
Neural-based Detectors
Paper
Venue
Date
Link
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
arXiv
2023-01
Multiscale Positive-Unlabeled Detection of AI-Generated Texts
ICLR 2024 (Spotlight)
2023-05
Real or fake? Learning to discriminate machine from human generated text
arXiv
2019-06
Automatic Detection of Generated Text is Easiest when Humans are Fooled
ACL 2020
2019-11
Stylometric Detection of AI-Generated Text in Twitter Timelines
arXiv
2023-03
TweepFake: about Detecting Deepfake Tweets
PLoS ONE
2020-07
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?
TALN 2023
2023-06
Deepfake Text Detection in the Wild
ACL 2024
2023-05
ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models
arXiv
2023-04
Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT
arXiv
2023-06
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
arXiv
2023-05
Neural Deepfake Detection with Factual Structure of Text
EMNLP 2020
2020-10
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
IJCNLP-AACL 2023
2023-09
RADAR: Robust AI-Text Detection via Adversarial Learning
NeurIPS 2023
2023-07
OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples
AAAI 2024
2023-07
Fighting fire with fire: Can chatgpt detect ai-generated text?
SIGKDD Explorations
2023-08
GPT Paternity Test: GPT Generated Text Detection with GPT Genetic Inheritance
arXiv
2023-05
Raidar: geneRative AI Detection viA Rewriting
ICLR 2024
2024-01
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT
EMNLP 2023
2023-12
Defending Against Neural Fake News by Removing LLMs' Greatest Weakness
NeurIPS 2019
2019-05
CoCo: Coherence-Enhanced Machine-Generated Text Detection
EMNLP 2023
2022-12
SeqXGPT: Sentence-Level AI-Generated Text Detection
EMNLP 2023
2023-10
J-Guard: Robust Guardrails against Unreliable Text Generation by LLMs
IJCNLP-AACL 2023
2023-09
DEMASQ: Unmasking the ChatGPT Wordsmith
NDSS 2024
2023-11
Smaller Language Models are Better Black-box Detectors
EACL 2024
2023-05
DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning
NeurIPS 2024
2024-10
A Ship of Theseus: Paraphrasing Capabilities for Detecting LLM-Generated Text
ACL 2024
2023-11
ReMoDetect: Reward Modified Detection of LLM-Generated Text
arXiv
2024-05
Origin Tracing and Detecting of Large Language Models
arXiv
2023-04
Text Fluoroscopy: Detecting LLM-Generated Text through Intrinsic Features
EMNLP 2024
2024-11
Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
NeurIPS 2025
2025-10
DAMAGE: Detecting Adversarially Modified AI Generated Text
arXiv
2025-01
NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution
arXiv
2026-03
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (DRGD)
arXiv
2026-04
GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization
ACL 2026 Findings
2026-07
Human-assisted Methods
Paper
Venue
Date
Link
RoFT: A Tool for Real vs Fake Text Detection
EMNLP 2020 Demo
2020-10
Human Heuristics for AI-Generated Language Are Flawed
arXiv
2022-06
Real or Fake Text? Investigating the Abilities of Language Models to Detect AI-Generated Text
AAAI 2023
2022-12
Does Human Collaboration Enhance the Accuracy and Reliability of AI-Generated Text Detection?
HCOMP 2023
2023-04
People cannot distinguish GPT-4 from a human in a Turing test
arXiv
2024-05
Detector Attack
Paper
Venue
Date
Link
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
NeurIPS 2023
2023-03
Can AI-Generated Text be Reliably Detected?
arXiv
2023-03
Red Teaming Language Model Detectors with Language Models
TACL 2023
2023-05
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
LREC-COLING 2024
2024-05
RAFT: Realistic Attacks to Fool Text Detectors
EMNLP 2024
2024-10
Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text
NeurIPS 2025
2025-06
CoPA: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors
EMNLP 2025
2025-05
Attacks on Machine-Text Detectors Retain Stylistic Fingerprints
arXiv
2025-05
Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks (SIRA)
ICML 2025
2025-05
DE-MARK: Watermark Removal in Large Language Models
ICML 2025
2024-10
MASH: Evading Black-Box AI-Generated Text Detectors via Style Humanization
ACL 2026 Findings
2026-01
Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs
arXiv
2026-05
AI Watermark Evidence Fails Forensic Readiness: An Empirical Evaluation
arXiv
2026-07
Shared Task
Paper
Venue
Date
Link
Findings of the RuATD Shared Task 2022 on Artificial Text Detection in Russian
Dialogue 2022
2022-06
SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection
SemEval 2024
2024-04
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection
COLING 2025
2025-01
Overview of the NLPCC 2025 Shared Task 1: LLM-Generated Text Detection
NLPCC 2025
2025-08
Findings of the Counter Turing Test (CT2): AI-Generated Text Detection
DeFactify 2025
2026-05
The Second Shared Task on LLM-Generated Text Detection (NLPCC 2026 Task 6)
NLPCC 2026
2026-11
Related Works
Other Surveys
Paper
Venue
Date
Link
Automatic Detection of Machine Generated Text: A Critical Survey
COLING 2020
2020-11
The Science of Detecting LLM-Generated Texts
arXiv
2023-02
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
ACM Trustworthy AI
2022-10
Computer-Generated Text Detection Using Machine Learning: A Systematic Review
Springer
-
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective
ACM SIGKDD Explorations
2022-10
Deepfake Text Detection: Limitations and Opportunities
S&P 2023
2022-10
A Survey of AI-generated Text Forensic Systems
arXiv
2024-03
SoK: Watermarking for AI-Generated Content
arXiv
2024-11
The Imitation Game Revisited: A Comprehensive Survey on Recent Advances in AI-generated Text Detection
ESWA 2025
2025-01
Watermarking for AI Content Detection: A Review
ICLR 2025 Workshop
2025-04
A Survey on LLM Watermarking: Theory and Deployment
arXiv
2026-07
🚩 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.
About
A survey and reflection on the latest research breakthroughs in LLM-generated Text detection, including data, detectors, metrics, current issues and future directions.
