Beat LLMs at their own game: Zero-shot LLM-generated text detection via querying ChatGPT
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Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
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The latest emerging black-box zero-shot methods demonstrate enhanced stability and application potential compared to white-box based zero-shot methods by extracting discriminative metrics that are independent of white-box models.
“We have observed that the latest emerging black-box zero-shot methods (Yang et al. 2023b; Mao et al. 2024; Zhu et al. 2023; Quidwai, Li, and Dube 2023; Guo and Yu 2023) demonstrate enhanced stability and application potential compared to whitebox based zero-shot methods by extracting discriminative metrics that are independent of white-box models.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Zhu et al. 2023
Section 4.2.1 reports consistently good detection performance across datasets for this black-box method, while zero-shot methods using a proxy model perform poorly on some datasets. The Introduction explains that the method avoids access to the source model’s logits or losses. But the paper evaluates this method, not black-box zero-shot methods generally, and its principal similarity metric, BARTScore-CNN, uses a BART model; it does not establish independence from all white-box models. (LLM, conf=0.84)
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works
GLTR: Statistical Detection and Visualization of Generated Text
Gehrmann, S., Strobelt, H., & Rush, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/p19-3019 · Cited by 470 · cited 8× · see citing works
“The Entropy method calculates the entropy of the softmax logits derived from the GPT-2-medium model and then averages the entropy of each token to get the final entropy score.”
BackgroundMethodInfluentialELI5: long form question answering
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1346 · Cited by 319 · see citing works
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023) · arXiv · doi:10.48550/arxiv.2301.11305 · Cited by 151 · see citing works
Efficient Attentions for Long Document Summarization
Huang, L., Cao, S., Parulian, N., Ji, H., & Wang, L. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.112 · Cited by 146 · see citing works
Flagged first, then by citations
Research works printed but not linked: 22
- Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc'Aurelio Ranzato, and Arthur Szlam. 2019. Real or fake? learning to discriminate ma- chine from human generated text. arXiv preprint arXiv:1906.03351. · arXiv
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems, volume 33, pages 1877-1901.
- Shu Chen, Zeqian Ju, Xiangyu Dong, Hongchao Fang, Sicheng Wang, Yue Yang, Jiaqi Zeng, Ruisi Zhang, Ruoyu Zhang, Meng Zhou, et al. 2020. Meddialog: a large-scale medical dialogue dataset. arXiv preprint arXiv:2004.03329. · arXiv
- Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019. Multi-news: A large-scale multi-document summarization dataset and abstrac- tive hierarchical model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1074-1084, Florence, Italy. Asso- ciation for Computational Linguistics. · DOI · Paper page
- Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023. How close is chatgpt to human experts? comparison corpus, evaluation, and detection. arXiv preprint arXiv:2301.07597. · arXiv
- Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang. 2023. Mgtbench: Benchmarking machine-generated text detection. arXiv preprint arXiv:2303.14822. · arXiv
- Karl Moritz Hermann, Tomas Kocisky, Edward Grefen- stette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. Advances in neural information processing systems, 28.
- Ganesh Jawahar, Muhammad Abdul-Mageed, and VS Laks Lakshmanan. 2020. Automatic detection of machine generated text: A critical survey. In Pro- ceedings of the 28th International Conference on Computational Linguistics, pages 2296-2309.
- Anastassia Kornilova and Vladimir Eidelman. 2019. BillSum: A corpus for automatic summarization of US legislation. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 48-56, Hong Kong, China. Association for Computational Linguistics. · DOI
- Chin-Yew Lin. 2004. ROUGE: A package for auto- matic evaluation of summaries. In Text Summariza- tion Branches Out, pages 74-81, Barcelona, Spain. Association for Computational Linguistics.
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint arXiv:1907.11692. · arXiv
- Aengus Lynch, Gbètondji JS Dovonon, Jean Kaddour, and Ricardo Silva. 2023. Spawrious: A benchmark for fine control of spurious correlation biases. arXiv preprint arXiv:2303.05470. · arXiv
- Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018. Www'18 open challenge: financial opinion mining and question answering. In Companion proceedings of the the web conference 2018, pages 1941-1942.
- Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuris- tics in natural language inference. In Proceedings of the 57th Annual Meeting of the Association for Com- putational Linguistics, pages 3428-3448, Florence, Italy. Association for Computational Linguistics. · DOI · Paper page
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computa- tional Linguistics, pages 311-318.
- Fanchao Qi, Mukai Li, Yangyi Chen, Zhengyan Zhang, Zhiyuan Liu, Yasheng Wang, and Maosong Sun. 2021. Hidden killer: Invisible textual backdoor at- tacks with syntactic trigger. In Proceedings of the 59th Annual Meeting of the Association for Compu- tational Linguistics and the 11th International Joint Conference on Natural Language Processing (Vol- ume 1: Long Papers), pages 443-453, Online. Asso- ciation for Computational Linguistics. · DOI
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Tr- ishala Neeraj, Jos Rozen, Abheesht Sharma, An- drea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022. Multi- task prompted training enables zero-shot task gener- alization. In International Conference on Learning Representations.
- Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Rad- ford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al. 2019. Release strategies and the so- cial impacts of language models. arXiv preprint arXiv:1908.09203. · arXiv
- Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. 2020. Authorship attribution for neural text gener- ation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8384-8395, Online. Association for Computational Linguistics. · DOI · Paper page
- Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text genera- tion. In Advances in Neural Information Processing Systems, volume 34, pages 27263-27277.
- Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Eval- uating text generation with bert. In International Conference on Learning Representations.
Cited by
Sorted by citations
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Wu, J., Yang, S., Zhan, R., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Computational Linguistics · doi:10.1162/coli_a_00549 · Cited by 212 · see citing works
- Authors
- Zhu, B., Yuan, L., Cui, G., Chen, Y., Fu, C., He, B., Deng, Y., Liu, Z., Sun, M., & Gu, M.
- Affiliations
- Tsinghua University (CN)
- University of Illinois Urbana-Champaign (US)
- Zhejiang University (CN)
- Corresponding author
- Deng, Yangdong
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 7470-7483
- OpenAlex
- W4389520675
- Cited by
- 16
- References
- 8
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 1
- FWCI
- 2.83
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (62236004)
- Tsinghua University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- LLM-generated text detection, ChatGPT, large language models, text forgery detection, generative AI
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