  Large language models (LLMs) have the potential to generate texts that pose
risks of misuse, such as plagiarism, planting fake reviews on e-commerce
platforms, or creating inflammatory false tweets. Consequently, detecting
whether a text is generated by LLMs has become increasingly important. Existing
high-quality detection methods usually require access to the interior of the
model to extract the intrinsic characteristics. However, since we do not have
access to the interior of the black-box model, we must resort to surrogate
models, which impacts detection quality. In order to achieve high-quality
detection of black-box models, we would like to extract deep intrinsic
characteristics of the black-box model generated texts. We view the generation
process as a coupled process of prompt and intrinsic characteristics of the
generative model. Based on this insight, we propose to decouple prompt and
intrinsic characteristics (DPIC) for LLM-generated text detection method.
Specifically, given a candidate text, DPIC employs an auxiliary LLM to
reconstruct the prompt corresponding to the candidate text, then uses the
prompt to regenerate text by the auxiliary LLM, which makes the candidate text
and the regenerated text align with their prompts, respectively. Then, the
similarity between the candidate text and the regenerated text is used as a
detection feature, thus eliminating the prompt in the detection process, which
allows the detector to focus on the intrinsic characteristics of the generative
model. Compared to the baselines, DPIC has achieved an average improvement of
6.76\% and 2.91\% in detecting texts from different domains generated by GPT4
and Claude3, respectively.
