SeqXGPT: Sentence-Level AI-Generated Text Detection

Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang, Dong Zhang, Xipeng Qiu


Abstract
Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level detection challenge by synthesizing a dataset that contains documents that are polished with LLMs, that is, the documents contain sentences written by humans and sentences modified by LLMs. Then we propose Sequence X (Check) GPT, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection. These features are composed like waves in speech processing and cannot be studied by LLMs. Therefore, we build SeqXGPT based on convolution and self-attention networks. We test it in both sentence and document-level detection challenges. Experimental results show that previous methods struggle in solving sentence-level AIGT detection, while our method not only significantly surpasses baseline methods in both sentence and document-level detection challenges but also exhibits strong generalization capabilities.
Anthology ID:
2023.emnlp-main.73
Original:
2023.emnlp-main.73v1
Version 2:
2023.emnlp-main.73v2
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1144–1156
Language:
URL:
https://aclanthology.org/2023.emnlp-main.73
DOI:
Bibkey:
Cite (ACL):
Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang, Dong Zhang, and Xipeng Qiu. 2023. SeqXGPT: Sentence-Level AI-Generated Text Detection. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1144–1156, Singapore. Association for Computational Linguistics.
Cite (Informal):
SeqXGPT: Sentence-Level AI-Generated Text Detection (Wang et al., EMNLP 2023)
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