Summary of Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with Guidelinellm, by Shaoqing Zhang et al.
Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
by Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Rongxiang Weng, Muyun Yang, Tiejun Zhao, Min Zhang
First submitted to arxiv on: 10 Dec 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 proposed GuidelineLLM paradigm assists large language models (LLMs) in recognizing queries that may have harmful content. This novel defensive approach identifies potential risks associated with a query, summarizes these risks into guideline suggestions, and feeds them to the responding LLMs before they respond. Unlike existing methods, GuidelineLLM eliminates the need for additional safety fine-tuning of the LLMs themselves, enhancing its general applicability across various LLMs. Experimental results show that GuidelineLLM can significantly reduce the attack success rate against LLMs (an average reduction of 34.17%) while maintaining their helpfulness in handling benign queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GuidelineLLM is a new way to help big language models be safer. Sometimes, these models can be tricked into saying bad things. GuidelineLLM helps by checking what someone is asking before the model responds. It looks for potential problems and gives suggestions to make sure the model stays safe. This approach makes it easier to use different language models without having to retrain them. The results show that this method can make a big difference in keeping language models safe from bad things. |
Keywords
» Artificial intelligence » Fine tuning
