ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
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Amrita Bhattacharjee, Tharindu Kumarage, Raha Moraffah, Huan Liu. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
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There is a trade-off: detectors robust against genre changes (e.g., ConDA) are poor at detecting MGT created with a model different from the one they were trained on, while detectors robust against changes in LLM (e.g., DEMASQ) fail when there is a change in genre.
“There is a trade-off where detectors that are robust against genre changes like ConDA (Bhattacharjee et al., 2023) (F1-score when trained with news dropped from 0.99 to 0.67 when testing essays) are poor at detecting MGT created with a model different than the one it was trained on, when trained with StableLM (StableLMTuned-Alpha, n.d.) the F1-score testing Claude drops to 0.00. On the other hand,”
From AI for scientific integrity: detecting ethical breaches, errors, and misconduct in manuscripts (2025) · cites this paper as Bhattacharjee et al., 2023
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- Authors
- Bhattacharjee, A., Kumarage, T., Moraffah, R., & Liu, H.
- Affiliations
- Arizona State University (US)
- Venue
- Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2023
- Pages
- 598-610
- OpenAlex
- W4392669928
- Cited by
- 43
- References
- 46
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 6
- FWCI
- 7.62
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) (publishedVersion, cc-by)
- Funders
- Defense Advanced Research Projects Agency (HR001120C0123)
- Office of Naval Research (N00014-21-1-4002, N00014)
- Army Research Office (W911NF2020124, W911NF2110030)
- 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
- AI-generated text detection, synthetic text detection, contrastive learning, domain adaptation
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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