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%0 Conference Proceedings
%T How Much Training Data Is Enough? Fine-Tuning LLMs for Short-Answer Scoring
%A McGrane, Joshua A.
%A Torres Irribarra, David
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F mcgrane-torres-irribarra-2026-much
%X Fine-tuned models approached state-of-the-art agreement using small labelled sets. Open-weight models met operational criteria on seven of ten items, with a median of 50 responses per score point among passing items. A single marking exercise may supply enough data for automated short-answer scoring.
%U https://aclanthology.org/2026.aimecon-wip.39/
%P 306-314

Markdown (Informal)

[How Much Training Data Is Enough? Fine-Tuning LLMs for Short-Answer Scoring](https://aclanthology.org/2026.aimecon-wip.39/) (McGrane & Torres Irribarra, AIME-Con 2026)

• How Much Training Data Is Enough? Fine-Tuning LLMs for Short-Answer Scoring (McGrane & Torres Irribarra, AIME-Con 2026)

ACL

• Joshua A McGrane and David Torres Irribarra. 2026. How Much Training Data Is Enough? Fine-Tuning LLMs for Short-Answer Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 306–314, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).