  ChatGPT and other general large language models (LLMs) have achieved
remarkable success, but they have also raised concerns about the misuse of
AI-generated texts. Existing AI-generated text detection models, such as based
on BERT and RoBERTa, are prone to in-domain over-fitting, leading to poor
out-of-domain (OOD) detection performance. In this paper, we first collected
Chinese text responses generated by human experts and 9 types of LLMs, for
which to multiple domains questions, and further created a dataset that mixed
human-written sentences and sentences polished by LLMs. We then proposed
LLM-Detector, a novel method for both document-level and sentence-level text
detection through Instruction Tuning of LLMs. Our method leverages the wealth
of knowledge LLMs acquire during pre-training, enabling them to detect the text
they generate. Instruction tuning aligns the model's responses with the user's
expected text detection tasks. Experimental results show that previous methods
struggle with sentence-level AI-generated text detection and OOD detection. In
contrast, our proposed method not only significantly outperforms baseline
methods in both sentence-level and document-level text detection but also
demonstrates strong generalization capabilities. Furthermore, since
LLM-Detector is trained based on open-source LLMs, it is easy to customize for
deployment.
