On the Zero-Shot Generalization of Machine-Generated Text Detectors

Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, Tianxing He

Abstract
The rampant proliferation of large language models, fluent enough to generate text indistinguishable from human-written language, gives unprecedented importance to the detection of machine-generated text. This work is motivated by an important research question: How will the detectors of machine-generated text perform on outputs of a new generator, that the detectors were not trained on? We begin by collecting generation data from a wide range of LLMs, and train neural detectors on data from each generator and test its performance on held-out generators. While none of the detectors can generalize to all generators, we observe a consistent and interesting pattern that the detectors trained on data from a medium-size LLM can zero-shot generalize to the larger version. As a concrete application, we demonstrate that robust detectors can be built on an ensemble of training data from medium-sized models.
Anthology ID:
2023.findings-emnlp.318
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4799–4808
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.318
DOI:
10.18653/v1/2023.findings-emnlp.318
Bibkey:
Cite (ACL):
Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, and Tianxing He. 2023. On the Zero-Shot Generalization of Machine-Generated Text Detectors. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4799–4808, Singapore. Association for Computational Linguistics.
Cite (Informal):
On the Zero-Shot Generalization of Machine-Generated Text Detectors (Pu et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2023.findings-emnlp.318.pdf
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