LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing
Zhengxiang Wang, Veronika Makarova, Zhi Li, Jordan Kodner, Owen Rambow
Abstract
The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human experts against 9 analytic criteria, we prompt several popular LLMs to perform the same task under various conditions. To evaluate the quality of feedback comments, we apply a novel feedback comment quality evaluation framework. This framework is interpretable, cost-efficient, scalable, and reproducible, compared to existing methods that rely on manual judgments. We find that LLMs can generate reasonably good and generally reliable multi-dimensional analytic assessments. We release our corpus and code for reproducibility.
BibTeX
@inproceedings{wang-etal-2025-llms-perform,
title = "{LLM}s can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of {L}2 Graduate-Level Academic {E}nglish Writing",
author = "Wang, Zhengxiang and
Makarova, Veronika and
Li, Zhi and
Kodner, Jordan and
Rambow, Owen",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.423/",
doi = "10.18653/v1/2025.acl-long.423",
pages = "8637--8663",
ISBN = "979-8-89176-251-0"
}