Summary of Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models, by Hongfu Liu et al.
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models
by Hongfu Liu, Yuxi Xie, Ye Wang, Michael Shieh
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a two-stage transfer learning framework called DeGCG to address the limitations of Greedy Coordinate Gradient (GCG) in language language models (LLMs). The framework decouples the search process into behavior-agnostic pre-searching and behavior-relevant post-searching. By employing direct first target token optimization, DeGCG facilitates efficient searching across various models and domains. The authors also introduce an interleaved variant called i-DeGCG, which iteratively leverages self-transferability to accelerate the search process. Experimental results on HarmBench demonstrate the efficiency of DeGCG and i-DeGCG in improving the performance of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making language models safer by finding ways to quickly find the right answers. This is important because bad people might try to use language models for mean things. The current way of doing this, called Greedy Coordinate Gradient (GCG), is slow and not very good. The researchers found a new way to do it that is faster and works better. They call it DeGCG, which stands for Decoupled Greedy Coordinate Gradient. It has two parts: one part makes sure the model doesn’t get confused, and the other part helps the model find the right answer quickly. They tested their new method on a special dataset called HarmBench and found that it worked really well. |
Keywords
» Artificial intelligence » Optimization » Token » Transfer learning » Transferability
