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        <title>Fine-tuning Large Language Models for Automated Scoring: Classification, Regression, vs. Ordinal Regression</title>
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            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
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    <abstract>Fine-tuning large language models for automated scoring is often formulated as either a regression or a classification task. However, scores assigned by human raters are on an ordinal scale. We summarize different deep-learning ordinal regression methods and compare their performances with those from regression and classification using a real dataset.</abstract>
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%0 Conference Proceedings
%T Fine-tuning Large Language Models for Automated Scoring: Classification, Regression, vs. Ordinal Regression
%A Zu, Jiyun
%A Badola, Akshay
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F zu-badola-2026-fine
%X Fine-tuning large language models for automated scoring is often formulated as either a regression or a classification task. However, scores assigned by human raters are on an ordinal scale. We summarize different deep-learning ordinal regression methods and compare their performances with those from regression and classification using a real dataset.
%U https://aclanthology.org/2026.aimecon-main.55/
%P 490-496

Markdown (Informal)

[Fine-tuning Large Language Models for Automated Scoring: Classification, Regression, vs. Ordinal Regression](https://aclanthology.org/2026.aimecon-main.55/) (Zu & Badola, AIME-Con 2026)

• Fine-tuning Large Language Models for Automated Scoring: Classification, Regression, vs. Ordinal Regression (Zu & Badola, AIME-Con 2026)

ACL

• Jiyun Zu and Akshay Badola. 2026. Fine-tuning Large Language Models for Automated Scoring: Classification, Regression, vs. Ordinal Regression. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 490–496, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).