CoTSRF：利用思维链作为大型语言模型的隐蔽且稳健的指纹
CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models
AI总结：
本文提出CoTSRF，利用思维链作为LLM指纹，通过对比学习提取特征并比较KL散度，实现隐蔽且稳健的指纹验证。
摘要：
尽管开源大型语言模型（LLM）提供了优越的性能，但它们容易受到滥用。为了解决这个问题，最近的工作提出了LLM指纹识别方法，以识别可疑应用背后的特定来源LLM。然而，这些方法未能提供隐蔽且稳健的指纹验证。在本文中，我们提出了一种新颖的LLM指纹识别方案，即CoTSRF，它利用思维链（CoT）作为LLM的指纹。CoTSRF首先通过使用精心设计的CoT查询来查询来源LLM，收集其响应。然后，它应用对比学习来训练一个CoT提取器，从响应中提取CoT特征（即指纹）。最后，CoTSRF通过比较来源LLM和可疑LLM的CoT特征之间的Kullback-Leibler散度与经验阈值来进行指纹验证。我们进行了各种实验，以证明我们提出的CoTSRF在LLM指纹识别方面的优势，特别是在隐蔽且稳健的指纹验证方面。
英文摘要：
Despite providing superior performance, open-source large language models (LLMs) are vulnerable to abusive usage. To address this issue, recent works propose LLM fingerprinting methods to identify the specific source LLMs behind suspect applications. However, these methods fail to provide stealthy and robust fingerprint verification. In this paper, we propose a novel LLM fingerprinting scheme, namely CoTSRF, which utilizes the Chain of Thought (CoT) as the fingerprint of an LLM. CoTSRF first collects the responses from the source LLM by querying it with crafted CoT queries. Then, it applies contrastive learning to train a CoT extractor that extracts the CoT feature (i.e., fingerprint) from the responses. Finally, CoTSRF conducts fingerprint verification by comparing the Kullback-Leibler divergence between the CoT features of the source and suspect LLMs against an empirical threshold. Various experiments have been conducted to demonstrate the advantage of our proposed CoTSRF for fingerprinting LLMs, particularly in stealthy and robust fingerprint verification.
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