AgenticEval: Self-Evolving Safety Evaluation for LLMs
A new multi-agent framework called AgenticEval proposes a paradigm shift in safety evaluation for Large Language Models (LLMs), moving from static benchmarks to a continuous, self-evolving process. The system autonomously ingests unstructured policy documents to generate and perpetually update safety benchmarks. It uses a pipeline of specialized agents and a Self-evolving Evaluation loop that learns from results to create more sophisticated test cases. Experiments show consistent effectiveness in addressing dynamic AI risks and evolving regulations.
Key facts
- AgenticEval is a multi-agent framework for LLM safety evaluation.
- It reframes evaluation as a continuous and self-evolving process.
- The system autonomously ingests unstructured policy documents.
- It generates and perpetually evolves a comprehensive safety benchmark.
- It uses a synergistic pipeline of specialized agents.
- It incorporates a Self-evolving Evaluation loop.
- The loop learns from evaluation results to craft more sophisticated test cases.
- Experiments demonstrate consistent effectiveness.
Entities
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