  The extraordinary performance of large language models (LLMs) heightens the
importance of detecting whether the context is generated by an AI system. More
importantly, while more and more companies and institutions release their LLMs,
the origin can be hard to trace. Since LLMs are heading towards the time of
AGI, similar to the origin tracing in anthropology, it is of great importance
to trace the origin of LLMs. In this paper, we first raise the concern of the
origin tracing of LLMs and propose an effective method to trace and detect
AI-generated contexts. We introduce a novel algorithm that leverages the
contrastive features between LLMs and extracts model-wise features to trace the
text origins. Our proposed method works under both white-box and black-box
settings therefore can be widely generalized to detect various LLMs.(e.g. can
be generalized to detect GPT-3 models without the GPT-3 models). Also, our
proposed method requires only limited data compared with the supervised
learning methods and can be extended to trace new-coming model origins. We
construct extensive experiments to examine whether we can trace the origins of
given texts. We provide valuable observations based on the experimental
results, such as the difficulty level of AI origin tracing, and the AI origin
similarities, and call for ethical concerns of LLM providers. We are releasing
all codes and data as a toolkit and benchmark for future AI origin tracing and
detecting studies. \footnote{We are releasing all available resource at
\url{https://github.com/OpenLMLab/}.}
