Generative AI Detection 2025
Synopsis
- Task: tba.
- Submission: Deployment on TIRA [submit]
- Input: tba. [download]
- Evaluation Measures: tba. [code]
- Baselines: tba. [code]
- Evaluation: tba. [code]
Task
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Data
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Evaluation
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>Submission
tba
Baselines
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Results
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Related Work
- Bevendorff, Janek, Xavier Bonet Casals, Berta Chulvi, Daryna Dementieva, Ashaf Elnagar, Dayne Freitag, Maik Fröbe, et al. 2024. Overview of PAN 2024: Multi-Author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification: Extended Abstract. In Lecture Notes in Computer Science, 3-10. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland.
- Uchendu, Adaku, Thai Le, Kai Shu, and Dongwon Lee. 2020. Authorship Attribution for Neural Text Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 8384-95. Online: Association for Computational Linguistics.
- Jakesch, Maurice, Jeffrey T. Hancock, and Mor Naaman. 2023. Human Heuristics for AI-Generated Language Are Flawed. Proceedings of the National Academy of Sciences of the United States of America 120 (11): e2208839120.
- Hans, Abhimanyu, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2024. Spotting LLMs with Binoculars: Zero-Shot Detection of Machine-Generated Text. arXiv [Cs.CL].
- Su, Jinyan, Terry Yue Zhuo, Di Wang, and Preslav Nakov. 2023. DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text. arXiv [Cs.CL].
- Mitchell, Eric, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn. 2023. DetectGPT: Zero-Shot Machine-Generated Text Detection Using Probability Curvature. arXiv [Cs.CL].
- Bao, Guangsheng, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang. 2023. Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature. arXiv [Cs.CL].
- Koppel, Moshe, and Jonathan Schler. 2004. Authorship Verification as a One-Class Classification Problem. In Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004, 489-95.
- Bevendorff, Janek, Benno Stein, Matthias Hagen, and Martin Potthast. 2019. Generalizing Unmasking for Short Texts. In Proceedings of the 2019 Conference of the North, 654-59. Stroudsburg, PA, USA: Association for Computational Linguistics.
- Sculley, D., and C. E. Brodley. 2006. Compression and Machine Learning: A New Perspective on Feature Space Vectors. In Data Compression Conference (DCC'06), 332-41. IEEE.
- Halvani, Oren, Christian Winter, and Lukas Graner. 2017. On the Usefulness of Compression Models for Authorship Verification. In ACM International Conference Proceeding Series. Vol. Part F1305. Association for Computing Machinery. https://doi.org/10.1145/3098954.3104050.
- Uchendu, Adaku, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee. 2021. TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, 2001-16. Stroudsburg, PA, USA: Association for Computational Linguistics.
- Schuster, Tal, Roei Schuster, Darsh J. Shah, and Regina Barzilay. 2020. The Limitations of Stylometry for Detecting Machine-Generated Fake News. Computational Linguistics 46 (2): 499-510.
- Sadasivan, Vinu Sankar, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023. Can AI-Generated Text Be Reliably Detected? arXiv [Cs.CL].
- Ippolito, Daphne, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020. Automatic Detection of Generated Text Is Easiest When Humans Are Fooled. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1808-22. Stroudsburg, PA, USA: Association for Computational Linguistics.
Task Committee
