Summary of Scans: Mitigating the Exaggerated Safety For Llms Via Safety-conscious Activation Steering, by Zouying Cao et al.
SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering
by Zouying Cao, Yifei Yang, Hai Zhao
First submitted to arxiv on: 21 Aug 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 The proposed Safety-Conscious Activation Steering (SCANS) method mitigates exaggerated safety concerns in Large Language Models (LLMs), allowing them to reject malicious queries while still responding to benign ones. SCANS extracts refusal steering vectors, anchors specific safety-critical layers, and tracks hidden state transitions to steer model behavior towards a balance between adequate safety and performance. Experiments demonstrate state-of-the-art results on XSTest and OKTest benchmarks without compromising defense capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Safety alignment is important for Large Language Models (LLMs) to defend against malicious instructions. However, research shows that aligned LLMs often reject helpful queries because they’re too focused on being safe. The proposed SCANS method helps with this problem by finding the reasons why an LLM says no and then adjusting its behavior to be safer without rejecting helpful queries. Tests show that SCANS works well and doesn’t make the models any worse at defending against bad instructions. |
Keywords
» Artificial intelligence » Alignment
