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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, quick

Summary

A new graph-based evaluation harness addresses the limitations of static benchmarks for domain-specific language models by transforming structured clinical guidelines into a queryable knowledge graph. This framework dynamically instantiates evaluation queries via graph traversal, ensuring complete coverage of guideline relationships, surface-form contamination resistance through combinatorial variation, and validity from expert-authored graph structure. Applied to the WHO IMCI guidelines, it generates clinically grounded multiple-choice questions across symptom recognition, treatment, severity classification, and follow-up care. Evaluation of five language models using this harness revealed systematic capability gaps, with models excelling in symptom recognition but showing lower accuracy on treatment protocols and clinical management decisions. The system supports continuous data regeneration as guidelines evolve and is generalizable to other domains with structured decision logic.

Key takeaway

For Machine Learning Engineers evaluating LLMs for critical domain-specific applications, you should consider adopting graph-based evaluation harnesses. This approach provides robust, continuously regenerable benchmarks that accurately reflect evolving guidelines and resist contamination. It will help you pinpoint specific model weaknesses, such as those found in treatment protocols, ensuring your LLMs meet stringent accuracy and reliability requirements before deployment.

Key insights

A graph-based evaluation harness dynamically generates comprehensive, contamination-resistant LLM benchmarks from structured guidelines, revealing domain-specific capability gaps.

Principles

• Dynamic generation ensures benchmark maintainability.
• Graph traversal guarantees complete guideline coverage.
• Combinatorial variation resists surface-form contamination.

Method

Transform structured guidelines into a queryable knowledge graph. Dynamically instantiate evaluation queries via graph traversal to generate multiple-choice questions for LLM assessment.

In practice

• Evaluate LLMs against evolving clinical protocols.
• Generate diverse questions from structured decision logic.
• Identify specific LLM weaknesses in domain tasks.

Topics

• LLM Evaluation
• Knowledge Graphs
• Clinical Guidelines
• Domain-Specific Evaluation
• Benchmark Generation
• Contamination Resistance

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.