Year: 2023
Type: conference-paper
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.1
Institution: Fudan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G2565984771, G460263832
OpenAlex ID: W4389519206