FP-VEC: Fingerprinting Large Language Models via Efficient Vector Addition
<|reference_start|>FP-VEC: Fingerprinting Large Language Models via Efficient Vector Addition: Training Large Language Models (LLMs) requires immense computational power and vast amounts of data. As a result, protecting the intellectual property of these models through fingerprinting is essential for ownership authentication. While adding fingerprints to LLMs through fine-tuning has been attempted, it remains costly and unscalable. In this paper, we introduce FP-VEC, a pilot study on using fingerprint vectors as an efficient fingerprinting method for LLMs. Our approach generates a fingerprint vector that represents a confidential signature embedded in the model, allowing the same fingerprint to be seamlessly incorporated into an unlimited number of LLMs via vector addition. Results on several LLMs show that FP-VEC is lightweight by running on CPU-only devices for fingerprinting, scalable with a single training and unlimited fingerprinting process, and preserves the model's normal behavior. The project page is available at https://fingerprintvector.github.io .<|reference_end|>
@article{xu2024fp-vec:,
    title={FP-VEC: Fingerprinting Large Language Models via Efficient Vector
  Addition},
    author={Zhenhua Xu, Wenpeng Xing, Zhebo Wang, Chang Hu, Chen Jie, Meng Han},
    journal={arXiv preprint arXiv:2409.08846},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08846},
    primaryClass={cs.CR cs.CL cs.LG}
}