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Automatic Detection of Generated Text is Easiest when Humans are Fooled
Open accessAbstract
Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text.The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their decisions.Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies-topk, nucleus sampling, and untruncated random sampling-and show that improvements in decoding methods have primarily optimized for fooling humans.This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems.We also show that though both human and automatic detector performance improve with longer excerpt length, even multi-sentence excerpts can fool expert human raters over 30% of the time.Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems.
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“LLMs typically exhibit less diversity in word usage compared to humans [Ippolito et al., 2020;Dugan et al., 2023].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Ippolito et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Pre-trained language models (PTMs), especially BERT-based architectures [Devlin et al., 2019] such as BERT [Ippolito et al., 2020;Fabien et al., 2020;Manolache et al., 2021], Sentence-BERT [Schlicht and de Paula, 2021;Rivera-Soto et al., 2021], and RoBERTa [Huertas-Tato et al., 2022], have proven effective for learning authorship representation.”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Ippolito et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, neural networkbased models such as BERT [Ippolito et al., 2020], Radar [Hu et al., 2023], and GPT-sentinel [Chen et al., 2023a] can also be applied.”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Ippolito et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, exposing evaluators to examples before evaluation enhances their detection capabilities, especially with longer samples.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Ippolito et al. 2020
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“Neural network-based detectors, particularly those utilizing BERT, have proven effective in distinguishing between human-written texts and those generated by GPT-2 [Ippolito et al., 2020;Liu et al., 2019].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Ippolito et al., 2020
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“In contrast, detection algorithms frequently outperform the human in this task [Ippolito et al., 2020].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Ippolito et al., 2020
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“Existing detectors also lack robustness to various factors, such as alternative decoding strategies [Ippolito et al., 2020], input sequence length [Solaiman et al., 2019], different prompts [Kumarage et al., 2023;Lu et al., 2023], and repetition penalties [Fishchuk and Braun, 2023].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Ippolito et al., 2020
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“Recent studies (Ippolito et al. 2020) indicated that human evaluators might not be as proficient as detection algorithms in recognizing LLM-generated text across various settings.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Ippolito et al. 2020
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“Ippolito et al. (2020) suggested that text perceived as high quality by humans tends to be more easily recognizable by detectors.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Ippolito et al. 2020
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 2×
“It propogates quickly (Vosoughi et al., 2018), sets political agendas”
BackgroundSocial Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
Luca, M., & Zervas, G. (2016) · Management Science · doi:10.1287/mnsc.2015.2304 · Cited by 1,263
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242
Adversarial Learning for Neural Dialogue Generation
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1230 · Cited by 955
Research works printed but not linked: 4
- Timothy Niven and Hung-Yu Kao. 2019. Probing neu- ral network comprehension of natural language ar- guments. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguis- tics, pages 4658-4664, Florence, Italy. Association for Computational Linguistics.
- Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In Proceedings of the 54th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715- 1725.
- Alan Turing. 1950. Computing machinery and intelligence-am turing. Mind, 59(236):433.
- Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. CoRR, abs/1905.12616. · arXiv
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Bu, X., Tang, M., Wang, J., Zhang, J., & Luo, P. H. (2026) · Information · doi:10.3390/info17040320 · Cited by 1
Loth, A. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795513.3807421 · Cited by 2
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Devitt, S. K., Pearce, T. R., Chowdhury, A. K., & Mengersen, K. L. (2022) · Routledge eBooks · doi:10.4324/9780367808952-66 · Cited by 1
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Thevakumar, J., & Thevakumar, L. (2025) · Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech) · doi:10.18653/v1/2025.dravidianlangtech-1.66 · Cited by 1
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Dankova, B. (2023) · Reinvention an International Journal of Undergraduate Research · doi:10.31273/reinvention.v16i1.964
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Puspo, S. S. C., Raihan, N., Goswami, D., Emran, A. N. B., Ganguly, A., & Uzuner, Ö. (2024) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2024.semeval-1.197
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Tanaka, Y., & Hashimoto, Y. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825960
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Le, L., & Tran, D. T. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825505
Abid, N. (2024) · Global Journal of Multidisciplinary Sciences and Arts · doi:10.70445/gjmdsa.1.1.2024.111-141
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Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_16
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YILDIZ, F. E., Karaarslan, E., & Aydın, Ö. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5364392
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Peng, Z., Fu, J., Zou, L., Zheng, L., Ren, Y., & Peng, G. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108025
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Comparing the Humanness of Machine-Generated and Human-Authored Text
Toney, A., Bode, L., Ventura, T., Wilcox, E., & Singh, L. (2026) · ACM Computing Surveys · doi:10.1145/3806206
Shreesh, S., Daga, M., Parikh, D., Bommineni, K. R., Mishra, S., Naole, S., & Ramu, S. P. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465628
Chaffin, A. (2023) · theses.fr (ABES) · doi:10.70675/5e4cd6f0z8697z4a0cz921dzd8592affa0a9
Détection des éléments générés automatiquement Contenu académique
Liyanage, V. P. (2024) · theses.fr (ABES) · doi:10.70675/80dff7a0z6e58z4151zb20cz6f0bc537ce85
LLMs Killed Q&A Stars? Analyzing the Impact of LLM-Generated Answers on an Online Q&A Platform
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Modelos pré-treinados de LLM e questões éticas face aos direitos autorais
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Sinha, N., Saravindu, S., Maitra, T., & Giri, D. (2026) · International Conference on Emerging Systems and Intelligent Computing (ESIC) · doi:10.1109/esic68176.2026.11495939
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Lestari, M. W., Ginting, A., Aminuddin, H., Cholish, C., & Hasnita, U. (2026) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v12i4.9637
Identifying AI text input in English Second Language Writing by Sentiment Analysis
Senior, J., & Thompson, N. L. (2025) · International Conference on Business Intelligence for Technology Innovation (ICBITI) · doi:10.1109/icbiti65527.2025.11500760
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Kutyła, M., Gryka, P., Kozłowski, M., Gradoń, K., & Janicki, A. (2026) · Poznań Studies in Contemporary Linguistics · doi:10.1515/psicl-2025-0063
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Ding, X. (2026) · International Conference on Artificial Intelligence, Digital Media Technology and Social Computing (ICAIDS) · doi:10.1145/3806262.3806266
Stacy, D., & Kong, L. (2026) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3746467.3801525
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Slavova, E., Parfenova, A., & Pfeffer, J. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795513.3810453
Topa, S. A. (2026) · International Journal of Research and Innovation in Social Science · doi:10.47772/ijriss.2026.100500277
Liao, G., Chai, L., Hou, Y., & Yue, Q. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.05.047
Luo, T., Xu, W., & Tang, R. (2026) · Jisuanji shenghuojia. · doi:10.54097/qgwkw932
Metadata
- Authors
- Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D.
- Affiliations
- University of Pennsylvania (US)
- Google (United States) (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 1808-1822
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3021153741
- Cited by
- 264
- References
- 31
- Citations by year
- 2026: 39 · 2025: 64 · 2024: 38 · 2023: 63 · 2022: 18 · 2021: 29 · 2020: 13
- FWCI
- 11.75
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (FA8750-19-2-1004, FA8750)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- AI-generated text detection, decoding strategies, top-k sampling, nucleus sampling, random sampling, human raters, text generation evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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