  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
\textbf{Seq}uence \textbf{X} (Check) \textbf{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 \textit{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.
