SELF: A Robust Singular Value and Eigenvalue Approach for LLM Fingerprinting

The paper introduces SELF, an intrinsic weight‑based fingerprinting method for large language models that uses singular value and eigenvalue decomposition of attention weights to generate unique, transformation‑invariant fingerprints. SELF claims to eliminate the need for input data, resist false claim attacks, and reduce false positives compared to existing techniques. Experiments show that SELF achieves high IP infringement detection accuracy and remains robust against quantization, pruning, and fine‑tuning modifications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• fine-tuning
• efficiency
• benchmarks
• safety