Cross-domain detection of GPT-2-generated technical text
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Juan Rodriguez, Todd Hay, David Gros, Zain Shamsi, Ravi Srinivasan. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
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These vulnerabilities can undermine technical expertise.
“undermine technical expertise (Rodriguez et al. 2022a; Aliman and Kester 2021)”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Rodriguez et al. 2022a
The Abstract says convincing synthetic text can undermine genuine research, and the Introduction says synthetic technical texts could hinder the scientific process. Those passages support a threat to scientific work, but they do not specifically say that technical expertise itself is undermined. (LLM, conf=0.78)
Experimental — accuracy not yet measuredLLM-generated text detectors often struggle to generalize to unseen domains.
“LLM-generated text detectors often struggle to generalize to unseen domains encountered during training [Pu et al., 2023b;Rodriguez et al., 2022b;Li et al., 2023a]”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Rodriguez et al., 2022b
Detectors based on RoBERTa exhibit significant potential for robustness, requiring as few as a few hundred labels to fine-tune and deliver impressive results.
“Despite this, detectors based on RoBERTa exhibit significant potential for robustness, requiring as few as a few hundred labels to fine-tune and deliver impressive results (Rodriguez et al. 2022b).”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Rodriguez et al. 2022b
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RAGNet: Enhancing Conversational AI in Medical Domain
Varshney, N., Mantoo, S., Wadhwa, M., Aatif, M., & Bhardwaj, S. (2025) · doi:10.1109/icetm63734.2025.11051911 · Cited by 1 · see citing works
Erkennungsverfahren für KI-generierte Texte: Überblick und Architekturentwurf
Pröhl, T., Mohrhardt, R., Förster, N., Putzier, E., & Zarnekow, R. (2024) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-024-01051-w
Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_16
MedAID-ML: A Multilingual Dataset of Biomedical Texts for Detecting AI-Generated Content
Styll, P., Llanos, L. C., Fernández-García, J., & Segura-Bédmar, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04354-2_5
Towards Robust Detection of AI-Generated Text and Image Using Language and Vision Neural Models
Baskota, R., Dahal, D., Karki, Y., Acharya, S., & Acharya, P. (2025) · European Journal of Applied Science Engineering and Technology · doi:10.59324/ejaset.2025.3(6).01
When Less is More: Minimal Prompts with LoRA for LLM Text Detection
Wang, S., Fang, R., Li, M., He, Z., & Song, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_22
Detecting AI Assistance in Abstract Complex Tasks
King, T., Gurney, N., Miller, J. H., & Ustun, V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13187-4_11
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
LLM-Recognizer: Generative–Discriminative Feature Fusion for Sentence-Level Detection
Wu, Y., & Li, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3444-8_37
Tefke, T., & Staudemeyer, R. C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32335-4_11
IG-Guided Synonym Substitution Attacks on IndoBERT AI Detection Across Domains
Amelia, A. N. Z., Muliono, Y., & Isa, S. M. (2026) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict71158.2026.11620875
Hybrid AI-Based Detection of LLM-Generated Phishing Emails
Ghawa, R., & Alhogail, A. A. (2026) · Electronics · doi:10.3390/electronics15153383 · Cited by 1 · see citing works
Ismakaeva, I. D., Senina, A. V., & Shapovalov, M. S. (2026) · Историческая информатика · doi:10.7256/2585-7797.2026.3.81047
- Authors
- Rodriguez, J., Hay, T., Gros, D., Shamsi, Z., & Srinivasan, R.
- Affiliations
- University of California, Davis (US)
- Applied Research Laboratories, The University of Texas at Austin (US)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 1213-1233
- OpenAlex
- W4287889356
- Cited by
- 40
- References
- 54
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 12 · 2023: 8
- FWCI
- 4.42
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- 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
- GPT-2, AI-generated text detection, technical texts, cross-domain detection, large language models, text forgery detection
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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