Conceitos essenciais
Model merging techniques can effectively undermine watermark-based IP protection for large language models, but model fingerprinting remains robust against such attacks.
Resumo
The paper investigates the robustness of two state-of-the-art IP protection techniques, Quantization Watermarking and Instructional Fingerprint, against various model merging algorithms such as Task Arithmetic, TIES-MERGING, and DARE.
Key highlights:
- Experimental results show that current LLM watermarking techniques cannot survive in merged models, while model fingerprinting techniques can.
- Attackers can successfully generate high-quality merged models that possess multiple capabilities by combining different expert models, but the watermark information is lost in the process.
- In contrast, the fingerprint information remains intact in the merged models, even when their performance matches or exceeds the baseline.
- The authors advocate for including model merging as a necessary consideration in assessing the robustness of LLM IP protection methods to promote the healthy development of the open-source LLM community.
Estatísticas
The number of eggs that Janet's ducks lay per day is 16.
Janet eats 3 eggs for breakfast every day.
Janet bakes muffins with 4 eggs every day.
The price of a fresh duck egg is $2.
Citações
"Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e.g., GPUs) or require the collection of specific training data."
"Uncertified model merging can infringe upon the Intellectual Property (IP) rights of the original upstream models."
"Experimental results indicate that current Large Language Model (LLM) watermarking techniques cannot survive in the merged models, whereas model fingerprinting techniques can."