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            <namePart type="given">Joshua</namePart>
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    <abstract>In this study, we evaluate several models that incorporate architectural modifications to overcome the length limitations of the standard transformer architecture using the Kaggle ASAP 2.0 dataset. The models considered in this study include fine-tuned versions of XLNet, Longformer, ModernBERT, Mamba, and Llama models.</abstract>
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%0 Conference Proceedings
%T Long context Automated Essay Scoring with Language Models
%A Ormerod, Christopher
%A Kehat, Gitit
%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 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F ormerod-kehat-2025-long
%X In this study, we evaluate several models that incorporate architectural modifications to overcome the length limitations of the standard transformer architecture using the Kaggle ASAP 2.0 dataset. The models considered in this study include fine-tuned versions of XLNet, Longformer, ModernBERT, Mamba, and Llama models.
%U https://aclanthology.org/2025.aimecon-main.5/
%P 35-42

Markdown (Informal)

[Long context Automated Essay Scoring with Language Models](https://aclanthology.org/2025.aimecon-main.5/) (Ormerod & Kehat, AIME-Con 2025)

• Long context Automated Essay Scoring with Language Models (Ormerod & Kehat, AIME-Con 2025)

ACL

• Christopher Ormerod and Gitit Kehat. 2025. Long context Automated Essay Scoring with Language Models. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 35–42, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).