arXiv: cs.AI preprintsInternational5 November 2026
Pairing Expert Critique with LLM-Driven Refinement: An Iterative Approach for Curating Quality AI Evaluation Datasets
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arXiv:2611.05749v1 Announce Type: cross Abstract: Evaluating and improving domain-specific LLM responses requires high-quality reference data and rubrics, yet expert authoring is slow and expensive, and synthetic datasets may fail to capture practitioner preferences unless they are grounded in expert judgment. We present SME-C (Subject Matter Expert Critique), a human-AI workflow that captures expertise through lightweight critiques of candidate responses and complements it with LLM-driven analysis and refinement to iteratively curate response datasets and evaluation rubrics. We validate SME-C
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