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AI Research
arXiv CS.AI·May 21·6 min read
Prompt2Fingerprint: Plug-and-Play LLM Fingerprinting via Text-to-Weight Generation
TL;DR
Large language models (LLMs) lack built-in identity verification, making it hard to confirm which model generated a response or detect unauthorized copies. Prompt2Fingerprint converts natural language prompts directly into unique model weights (numerical parameters that define behavior), creating a plug-and-play fingerprinting system that embeds identifying signatures into any LLM without retraining.
✦ Why It Matters
Engineers can now verify LLM authenticity and detect model theft by embedding recoverable identity markers without expensive retraining.
Key Takeaways
- Adopt P2F for efficient LLM ownership management.
- Reduce computational costs associated with model fingerprinting.
- Utilize text descriptions for quick parameter adjustments.
- Enhance model tracking without extensive retraining.
How It Works
P2F reformulates the fingerprinting process by using a generator that maps textual descriptions directly to low-rank parameter increments. This allows for a single forward pass to inject fingerprints into LLMs, eliminating the need for separate training sessions for each identity.