From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs
Summary: 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 Need for Comprehensive Evaluation
As the use of language models (LLMs) expands in specialized fields such as healthcare, the necessity for rigorous evaluation frameworks becomes paramount. Traditional evaluation methods often rely on static datasets that lack adaptability and relevance to real-world applications. This presents significant challenges, particularly in domains where guidelines and best practices continuously evolve.
Introducing the Graph-Based Evaluation Harness
The innovative graph-based evaluation harness offers a transformative approach by converting structured clinical guidelines into a dynamic, queryable knowledge graph. This ensures that evaluators can generate relevant and contextually appropriate queries through graph traversal techniques. The framework is designed to address three key challenges in evaluation:
- Complete Coverage of Guideline Relationships: The harness guarantees that all relationships defined in clinical guidelines are comprehensively evaluated, minimizing the risk of oversight.
- Contamination Resistance: By employing combinatorial variation, the framework mitigates the risk of surface-form contamination, ensuring that the evaluation remains robust against biases introduced by static datasets.
- Validity from Expert Knowledge: The graph structure is derived from expert-authored guidelines, lending a level of validity and reliability that is often absent in manually curated datasets.
Application to WHO IMCI Guidelines
When applied to the World Health Organization’s Integrated Management of Childhood Illness (IMCI) guidelines, the evaluation harness successfully generates clinically grounded multiple-choice questions. These questions encompass various aspects of clinical practice, including:
- Symptom recognition
- Treatment protocols
- Severity classification
- Follow-up care
Findings from Evaluation
Evaluation across five different language models revealed systematic capability gaps. The models exhibited strong performance in symptom recognition tasks yet displayed lower accuracy when tasked with treatment protocols and clinical management decisions. These findings highlight the need for enhanced training and evaluation mechanisms in these critical areas.
Future Directions
The graph-based evaluation harness not only supports the continuous regeneration of evaluation data as clinical guidelines evolve but also demonstrates versatility across various domains featuring structured decision logic. This positions the framework as a scalable foundation for future evaluation infrastructures, fostering more effective and reliable assessments of language models in specialized applications.
Conclusion
In conclusion, the development of a graph-based evaluation harness marks a significant advancement in the evaluation of domain-specific language models. By ensuring comprehensive coverage, contamination resistance, and inherent validity, this innovative framework holds the potential to enhance the reliability and applicability of language models across diverse fields, particularly in healthcare.