We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| thuccslab/mergeguard | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| is_number | Ran | thuccslab/mergeguard/eval_math.py pointer only (licence: NONE) · get_code("0fe9cf6c00ef56ea") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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. Instead, it involves editing different upstream model parameters to absorb their downstream task capabilities. However, uncertified model merging can infringe upon the Intellectual Property (IP) rights of the original upstream models. In this paper, we conduct the first study on the robustness of IP protection methods under model merging scenarios. Specifically, we investigate two state-of-the-art IP protection techniques: Quantization Watermarking and Instructional Fingerprint, along with various advanced model merging technologies, such as Task Arithmetic, TIES-MERGING, and so on. Experimental results indicate that current Large Language Model (LLM) watermarking techniques cannot survive in the merged models, whereas model fingerprinting techniques can. Our research aims to highlight that model merging should be an indispensable consideration in the robustness assessment of model IP protection techniques, thereby promoting the healthy development of the open-source LLM community. Our code is available at https://github.com/ThuCCSLab/MergeGuard.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.05188")
get_code_for_paper("2404.05188")
have("2404.05188")
Connect an agent — have() is free.