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Abstract
In this work, we discuss different threat scenarios from neural fake news generated by state-of-the-art language models. Through our experiments, we assess the performance of generated text detection systems under these threat scenarios. For each scenario, we also identify the minimax strategy for the detector that minimizes its worst-case performance. This constitutes a set of best practices that practitioners can rely on. In our analysis, we find that detectors are prone to shortcut learning (lack of out-of-distribution generalization) and discuss approaches to mitigate this problem and improve detectors more broadly. Finally, we argue that strong detectors should be released along with new generators.- Anthology ID:
- 2022.coling-1.106
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1233–1249
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.106/
- DOI:
- Bibkey:
- Cite (ACL):
- Artidoro Pagnoni, Martin Graciarena, and Yulia Tsvetkov. 2022. Threat Scenarios and Best Practices to Detect Neural Fake News. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1233–1249, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Threat Scenarios and Best Practices to Detect Neural Fake News (Pagnoni et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.106.pdf
- Code
- artidoro/detect-gentext
- Data
- LAMBADA, WebText
Export citation
@inproceedings{pagnoni-etal-2022-threat,
title = "Threat Scenarios and Best Practices to Detect Neural Fake News",
author = "Pagnoni, Artidoro and
Graciarena, Martin and
Tsvetkov, Yulia",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.106/",
pages = "1233--1249",
abstract = "In this work, we discuss different threat scenarios from neural fake news generated by state-of-the-art language models. Through our experiments, we assess the performance of generated text detection systems under these threat scenarios. For each scenario, we also identify the minimax strategy for the detector that minimizes its worst-case performance. This constitutes a set of best practices that practitioners can rely on. In our analysis, we find that detectors are prone to shortcut learning (lack of out-of-distribution generalization) and discuss approaches to mitigate this problem and improve detectors more broadly. Finally, we argue that strong detectors should be released along with new generators."
}
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%0 Conference Proceedings %T Threat Scenarios and Best Practices to Detect Neural Fake News %A Pagnoni, Artidoro %A Graciarena, Martin %A Tsvetkov, Yulia %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F pagnoni-etal-2022-threat %X In this work, we discuss different threat scenarios from neural fake news generated by state-of-the-art language models. Through our experiments, we assess the performance of generated text detection systems under these threat scenarios. For each scenario, we also identify the minimax strategy for the detector that minimizes its worst-case performance. This constitutes a set of best practices that practitioners can rely on. In our analysis, we find that detectors are prone to shortcut learning (lack of out-of-distribution generalization) and discuss approaches to mitigate this problem and improve detectors more broadly. Finally, we argue that strong detectors should be released along with new generators. %U https://aclanthology.org/2022.coling-1.106/ %P 1233-1249
Markdown (Informal)
[Threat Scenarios and Best Practices to Detect Neural Fake News](https://aclanthology.org/2022.coling-1.106/) (Pagnoni et al., COLING 2022)
- Threat Scenarios and Best Practices to Detect Neural Fake News (Pagnoni et al., COLING 2022)
ACL
- Artidoro Pagnoni, Martin Graciarena, and Yulia Tsvetkov. 2022. Threat Scenarios and Best Practices to Detect Neural Fake News. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1233–1249, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.