  Generated texts from large language models (LLMs) are remarkably close to
high-quality human-authored text, raising concerns about their potential misuse
in spreading false information and academic misconduct. Consequently, there is
an urgent need for a highly practical detection tool capable of accurately
identifying the source of a given text. However, existing detection tools
typically rely on access to LLMs and can only differentiate between
machine-generated and human-authored text, failing to meet the requirements of
fine-grained tracing, intermediary judgment, and rapid detection. Therefore, we
propose LLMDet, a model-specific, secure, efficient, and extendable detection
tool, that can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and
others. In LLMDet, we record the next-token probabilities of salient n-grams as
features to calculate proxy perplexity for each LLM. By jointly analyzing the
proxy perplexities of LLMs, we can determine the source of the generated text.
Experimental results show that LLMDet yields impressive detection performance
while ensuring speed and security, achieving 98.54% precision and x5.0 faster
for recognizing human-authored text. Additionally, LLMDet can effortlessly
extend its detection capabilities to a new open-source model. We will provide
an open-source tool at https://github.com/TrustedLLM/LLMDet.

Computation and Language