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deepmind.google·2h ago
AI Research
arXiv CS.AI·3d ago·5 min read
Attention Sees, MLP Refuses: Tracing the Attention-to-MLP Safety Pathway in LLMs
TL;DR
A new framework, Sentinel, was developed to improve the safety of large language models (LLMs) by combining attention-derived perception scores with MLP-derived decision scores. This approach effectively reduces errors related to jailbreaks and over-refusals in LLMs.
✦ Why It Matters
Implement the Sentinel framework to enhance the safety and reliability of your LLM applications today.
Key Takeaways
- Utilize the Sentinel framework for improved LLM safety.
- Focus on attention updates for harmfulness detection.
- Enhance MLP updates post-training for better refusal writing.
- Combine multiple signals for lower error rates in LLM responses.
How It Works
Sentinel integrates two types of scores: an attention-derived perception score (P) that assesses harmfulness and an MLP-derived decision score (D) that determines refusal actions. By analyzing updates in the model's architecture, the framework effectively distinguishes between harmfulness recognition and the decision-making process that leads to refusals.
This dual approach allows for more nuanced responses and reduces the likelihood of over-refusals.