The exorbitant cost of training Large language models (LLMs) from scratch
makes it essential to fingerprint the models to protect intellectual property
via ownership authentication and to ensure downstream users and developers
comply with their license terms (e.g. restricting commercial use). In this
study, we present a pilot study on LLM fingerprinting as a form of very
lightweight instruction tuning. Model publisher specifies a confidential
private key and implants it as an instruction backdoor that causes the LLM to
generate specific text when the key is present. Results on 11 popularly-used
LLMs showed that this approach is lightweight and does not affect the normal
behavior of the model. It also prevents publisher overclaim, maintains
robustness against fingerprint guessing and parameter-efficient training, and
supports multi-stage fingerprinting akin to MIT License. Code is available in
this https URL

大规模语言模型的高昂训练成本使指纹识别模型保护知识产权成为必要，同时确保下游用户和开发者遵守许可条款，本研究提出了一种针对语言模型的指纹识别的试点研究，作为一种非常轻量级的指令调整，以提供模型许可者的身份验证，然而仅在密钥存在时才使语言模型生成特定文本，通过对 11 种常用的语言模型进行测试，结果显示该方法轻巧且不影响模型的正常行为，同时防止了许可者的过度声明，对指纹猜测和参数高效训练具有鲁棒性，并支持与 MIT 许可证类似的多阶段指纹识别。