  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
https://cnut1648.github.io/Model-Fingerprint/.
