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New workflow uses expert critique and LLMs to build AI evaluation datasets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new workflow called SME-C (Subject Matter Expert Critique) to efficiently create high-quality evaluation datasets for domain-specific LLMs. This approach combines lightweight critiques from human experts with LLM-driven analysis and refinement, significantly improving response quality compared to baseline methods. The system was validated in the financial business brief domain, demonstrating that critique-based refinement yields more domain-grounded improvements, while vote-based refinement is better at removing irrelevant content. This iterative process allows for the creation of robust evaluation resources more quickly and cost-effectively. AI

IMPACT This iterative approach to dataset curation could accelerate the development and benchmarking of domain-specific LLMs.

RANK_REASON The item is an academic paper detailing a new methodology for creating AI evaluation datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New workflow uses expert critique and LLMs to build AI evaluation datasets

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The item is an academic paper detailing a new methodology for creating AI evaluation datasets. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Honson Ling, Kidus Admassu, Stephanie Rosenthal ·

    Pairing Expert Critique with LLM-Driven Refinement: An Iterative Approach for Curating Quality AI Evaluation Datasets

    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 a…

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