From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs
📰 ArXiv cs.AI
Graph-based evaluation harness for domain-specific LLM evaluation transforms clinical guidelines into a queryable knowledge graph
Action Steps
- Transform structured clinical guidelines into a queryable knowledge graph
- Instantiate evaluation queries via graph traversal
- Evaluate LLM performance using the dynamically generated queries
- Refine the evaluation framework based on the results
Who Needs to Know This
AI engineers and researchers benefit from this approach as it provides a comprehensive and maintainable evaluation framework for domain-specific LLMs, enabling them to assess model performance more accurately
Key Insight
💡 Graph-based evaluation can provide comprehensive and contamination-resistant benchmarks for domain-specific LLMs
Share This
📈 Graph-based evaluation harness for LLMs provides guarantees for domain-specific evaluation
Key Takeaways
Graph-based evaluation harness for domain-specific LLM evaluation transforms clinical guidelines into a queryable knowledge graph
Full Article
Title: From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs
Abstract:
arXiv:2508.20810v2 Announce Type: replace Abstract: Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static, manually curated datasets do not satisfy these properties. We present a graph-based evaluation harness that transforms structured clinical guidelines into a queryable knowledge graph and dynamically instantiates evaluation queries via graph traversal. The framework provides three guarantees: (1) c
Abstract:
arXiv:2508.20810v2 Announce Type: replace Abstract: Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static, manually curated datasets do not satisfy these properties. We present a graph-based evaluation harness that transforms structured clinical guidelines into a queryable knowledge graph and dynamically instantiates evaluation queries via graph traversal. The framework provides three guarantees: (1) c
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