From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs

Researchers developed a graph-based evaluation framework that transforms clinical guidelines into dynamic benchmarks for testing domain-specific language models. The system addresses key evaluation challenges by providing contamination resistance, comprehensive coverage, and maintainable assessment tools that reveal systematic capability gaps in current AI models.

• →New graph-based framework dynamically generates evaluation queries from structured clinical guidelines to test language models.
• →System provides three key guarantees: complete coverage, contamination resistance, and inherited validity from expert-authored structures.
• →Testing on WHO IMCI guidelines revealed models perform well on symptom recognition but struggle with treatment protocols and clinical decisions.
• →Framework supports continuous regeneration of evaluation data as guidelines evolve and can generalize to other structured domains.
• →Research addresses critical need for rigorous, maintainable benchmarks in domain-specific AI evaluation infrastructure.