UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification

• URL: http://arxiv.org/abs/2410.12318v1
• Date: Wed, 16 Oct 2024 07:36:57 GMT
• Title: UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification
• Authors: Jiacheng Cai, Jiahao Yu, Yangguang Shao, Yuhang Wu, Xinyu Xing,
• Abstract summary: Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. In this paper, we introduce a novel and efficient approach to fingerprinting LLMs by leveraging under-trained tokens. Our method has minimal overhead and impact on model's performance, and does not require white-box access to target model's ownership identification.
• Score: 23.164580168870682
• License:
• Abstract: Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often require significant computational overhead or white-box verification access. In this paper, we introduce UTF, a novel and efficient approach to fingerprinting LLMs by leveraging under-trained tokens. Under-trained tokens are tokens that the model has not fully learned during its training phase. By utilizing these tokens, we perform supervised fine-tuning to embed specific input-output pairs into the model. This process allows the LLM to produce predetermined outputs when presented with certain inputs, effectively embedding a unique fingerprint. Our method has minimal overhead and impact on model's performance, and does not require white-box access to target model's ownership identification. Compared to existing fingerprinting methods, UTF is also more effective and robust to fine-tuning and random guess.

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