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        <title>Judge, Don’t Write: Scaling Golden Data Generation via Psychometric Standard-Setting</title>
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    <abstract>Human-written references are typically treated as the upper-bound gold standard for fine-tuning and evaluation, but writing them is an inefficient use of subject-matter expert (SME) time. To best use limited SME attention, we apply psychometric standard-setting to discover acceptance criteria for GenAI applications. These acceptance boundaries are themselves useful for calibrating an LLM-as-a-judge, model tuning, and defensible production monitoring. We report a case study on summarization across two domains at a large US financial institution. While summarization is a classic GenAI use case, what counts as acceptable differs significantly across domains. 25 SMEs across three panels produced 2,500+ judgments and yielded calibrated thresholds in an estimated 10x less SME time than the standard playbook of writing golden reference (goldens) directly. In both domains, LLM-generated goldens are preferred over SME-written ones ~80% of the time. Limited SME attention is best used to judge candidate goldens rather than write them.</abstract>
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%0 Conference Proceedings
%T Judge, Don’t Write: Scaling Golden Data Generation via Psychometric Standard-Setting
%A Novotney, Scott
%A Yin, Youbing
%A Babinsky, Erin
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F novotney-etal-2026-judge
%X Human-written references are typically treated as the upper-bound gold standard for fine-tuning and evaluation, but writing them is an inefficient use of subject-matter expert (SME) time. To best use limited SME attention, we apply psychometric standard-setting to discover acceptance criteria for GenAI applications. These acceptance boundaries are themselves useful for calibrating an LLM-as-a-judge, model tuning, and defensible production monitoring. We report a case study on summarization across two domains at a large US financial institution. While summarization is a classic GenAI use case, what counts as acceptable differs significantly across domains. 25 SMEs across three panels produced 2,500+ judgments and yielded calibrated thresholds in an estimated 10x less SME time than the standard playbook of writing golden reference (goldens) directly. In both domains, LLM-generated goldens are preferred over SME-written ones ~80% of the time. Limited SME attention is best used to judge candidate goldens rather than write them.
%U https://aclanthology.org/2026.emnlp-industry.162/
%P 2751-2761

Markdown (Informal)

[Judge, Don’t Write: Scaling Golden Data Generation via Psychometric Standard-Setting](https://aclanthology.org/2026.emnlp-industry.162/) (Novotney et al., EMNLP 2026)

• Judge, Don’t Write: Scaling Golden Data Generation via Psychometric Standard-Setting (Novotney et al., EMNLP 2026)

ACL

• Scott Novotney, Youbing Yin, and Erin Babinsky. 2026. Judge, Don’t Write: Scaling Golden Data Generation via Psychometric Standard-Setting. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2751–2761, Budapest, Hungary. Association for Computational Linguistics.