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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 3·5 min read
FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs
TL;DR
FUSE is a modular framework designed to evaluate the dangerous capabilities of large language models (LLMs) through three distinct pipelines: Knowledge, Defense, and Harm. It reveals significant differences in safety profiles across various LLMs, indicating that newer models do not consistently improve safety despite advancements in knowledge and alignment.
✦ Why It Matters
Evaluate your LLMs using the FUSE framework to better understand their dangerous capabilities today.
Key Takeaways
- Implement the FUSE framework for comprehensive LLM safety evaluations.
- Analyze the Knowledge, Defense, and Harm profiles of your models.
- Track the evolution of model capabilities over time.
- Compare safety profiles across different LLM families.
How It Works
FUSE operates by evaluating LLMs through three distinct pipelines: Knowledge assesses the model's information base, Defense measures its ability to resist generating harmful content, and Harm evaluates the actual harmful outputs produced. Each pipeline is modular, allowing for the integration of various scenario seeds and judge rubrics, while maintaining a consistent core evaluation engine across different domains.