@inproceedings{north-ormerod-2026-generative,
title = "Generative Language Models for Argumentation Annotation",
author = "North, Kai and
Ormerod, Christopher",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting-Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
month = oct,
year = "2026",
address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2026.aimecon-main.43/",
pages = "387--397",
ISBN = "979-8-9983004-0-0",
abstract = "This study explores the integration of a generative language model (GLM) into an Automated Writing Evaluation (AWE) system designed to highlight both argumentative components and errors in spelling and grammar. We fine-tune an open-source GLM using parameter-efficient techniques. We evaluate the model{'}s capabilities in both argument analysis and error detection against established datasets. We demonstrate that a single GLM with parameter-efficient adapters can accurately identify argumentative clauses, classify their types, map relationships between them, and flag mechanical mistakes. We establish that our AWE system performs at human-level accuracy, while only requiring a fraction of the computational power of much larger models."
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%0 Conference Proceedings
%T Generative Language Models for Argumentation Annotation
%A North, Kai
%A Ormerod, Christopher
%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 north-ormerod-2026-generative
%X This study explores the integration of a generative language model (GLM) into an Automated Writing Evaluation (AWE) system designed to highlight both argumentative components and errors in spelling and grammar. We fine-tune an open-source GLM using parameter-efficient techniques. We evaluate the model’s capabilities in both argument analysis and error detection against established datasets. We demonstrate that a single GLM with parameter-efficient adapters can accurately identify argumentative clauses, classify their types, map relationships between them, and flag mechanical mistakes. We establish that our AWE system performs at human-level accuracy, while only requiring a fraction of the computational power of much larger models.
%U https://aclanthology.org/2026.aimecon-main.43/
%P 387-397
Markdown (Informal)
[Generative Language Models for Argumentation Annotation](https://aclanthology.org/2026.aimecon-main.43/) (North & Ormerod, AIME-Con 2026)
ACL
- Kai North and Christopher Ormerod. 2026. Generative Language Models for Argumentation Annotation. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 387–397, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).