Summary of Seas: Self-evolving Adversarial Safety Optimization For Large Language Models, by Muxi Diao et al.
SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models
by Muxi Diao, Rumei Li, Shiyang Liu, Guogang Liao, Jingang Wang, Xunliang Cai, Weiran Xu
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces the SEAS (Self-Evolving Adversarial Safety) optimization framework to enhance the security of large language models (LLMs). The framework consists of three iterative stages: Initialization, Attack, and Adversarial Optimization. By leveraging data generated by the model itself, SEAS improves robustness and safety. The authors demonstrate the effectiveness of SEAS by comparing the performance of the Target model with GPT-4 and achieving a similar security level after three iterations. Additionally, they show a marked increase in attack success rate (ASR) against advanced models using the Red Team model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure large language models are safe to use. These models can be very smart, but sometimes they might say or do things that are not good. To fix this problem, the authors created a new way to test and improve these models’ security. They call it SEAS (Self-Evolving Adversarial Safety). It’s like playing a game where the model tries to defend itself against attacks. The authors show that their method works well and can even match the security of very advanced models. |
Keywords
» Artificial intelligence » Gpt » Optimization
