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Generated texts from large language models (LLMs) are remarkably close tohigh-quality human-authored text, raising concerns about their potential misusein spreading false information and academic misconduct. Consequently, there isan urgent need for a highly practical detection tool capable of accuratelyidentifying the source of a given text. However, existing detection toolstypically rely on access to LLMs and can only differentiate betweenmachine-generated and human-authored text, failing to meet the requirements offine-grained tracing, intermediary judgment, and rapid detection. Therefore, wepropose LLMDet, a model-specific, secure, efficient, and extendable detectiontool, that can source text from specific LLMs, such as GPT-2, OPT, LLaMA, andothers. In LLMDet, we record the next-token probabilities of salient n-grams asfeatures to calculate proxy perplexity for each LLM. By jointly analyzing theproxy perplexities of LLMs, we can determine the source of the generated text.Experimental results show that LLMDet yields impressive detection performancewhile ensuring speed and security, achieving 98.54% precision and x5.0 fasterfor recognizing human-authored text. Additionally, LLMDet can effortlesslyextend its detection capabilities to a new open-source model. We will providean open-source tool at https://github.com/TrustedLLM/LLMDet.

LLMDet: A Third Party Large Language Models Generated Text Detection Tool