Summary of Dynamic Guided and Domain Applicable Safeguards For Enhanced Security in Large Language Models, by Weidi Luo and He Cao and Zijing Liu and Yu Wang and Aidan Wong and Bing Feng and Yuan Yao and Yu Li
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models
by Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bing Feng, Yuan Yao, Yu Li
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel defense framework, called Guide for Defense (G4D), is proposed to address the limitations of existing methods in ensuring the safety of Large Language Models (LLMs) in domain-specific scenarios like chemistry. The G4D framework leverages accurate external information to provide unbiased summary of user intentions and analytically grounded safety response guidance. Experimental results show that G4D can enhance LLM’s robustness against jailbreak attacks on general and domain-specific scenarios without compromising the model’s functionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are getting more popular, but they need to be kept safe from bad actors. Some defense methods don’t work well in certain situations, like chemistry, where they might give harmful answers if not careful. Other methods can make LLMs too defensive and unhelpful. To fix this, researchers created a new framework called Guide for Defense (G4D). It uses good information to understand what users want and provide helpful safety tips. The results show that G4D makes LLMs safer without making them less useful. |
