Protecting Intellectual Property of Large Language Models through Instructional Fingerprinting

The exorbitant cost of training large language models (LLMs) makes it essential to fingerprint the models to protect intellectual property via ownership authentication and ensure downstream users comply with license terms. The proposed Instructional Fingerprinting (IF) method uses lightweight instruction tuning to implant a confidential private key as a backdoor, causing the LLM to generate specific text when the key is present. This approach is effective, persistent, efficient, robust, and reliable in fingerprinting 11 popular LLMs.