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Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification

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Briefings

This study identifies fundamental limitations in retrieval-based open-ended evaluation for medical fact verification. Using the MedExpert dataset and three closed-ended datasets, the authors decompose failures into retrieval-stage errors across five dimensions and verifier-reasoning errors across six steps. Despite scaling model size, adding reasoning effort, expanding to authoritative web sources, and applying medical fine-tuning, these improvements failed to resolve the core failure modes. The study concludes that these issues represent intrinsic limitations of the retrieve-then-verify paradigm in open-ended medical settings rather than artifacts of outdated systems, and the findings are released via an automatic pattern induction pipeline using LLM-as-Judge.

Key points

• The study introduces two comprehensive taxonomies decomposing retrieval-stage errors into five quality dimensions and verifier-reasoning errors into six consecutive steps.
• Scaling model size, adding reasoning effort, expanding to authoritative web sources, and applying medical fine-tuning failed to resolve the fundamental failure modes identified in the analysis.

Context and limitations

The analysis is based on a case study of the open-ended MedExpert dataset and three closed-ended datasets, with results released via an anonymous preprint link.

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