UTF: Undertrained Tokens as Fingerprints —— A Novel Approach to LLM Identification
Track: Long Paper Track (up to 9 pages)
Keywords: Large Language Model, fingerprint
TL;DR: We propose a lightweight, efficient and robust way to fingerprint LLMs with undertrained tokens.
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.
Submission Number: 137
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