@inproceedings{su-etal-2025-essayjudge,
title = "{E}ssay{J}udge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models",
author = "Su, Jiamin and
Yan, Yibo and
Fu, Fangteng and
Han, Zhang and
Ye, Jingheng and
Liu, Xiang and
Huo, Jiahao and
Zhou, Huiyu and
Hu, Xuming",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.329/",
doi = "10.18653/v1/2025.findings-acl.329",
pages = "6363--6389",
ISBN = "979-8-89176-256-5",
abstract = "Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (i) reliance on handcrafted features that limit generalizability, (ii) difficulty in capturing fine-grained traits like coherence and argumentation, and (iii) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose **EssayJudge**, the **first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits**. By leveraging MLLMs' strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research. Our dataset and code will be available upon acceptance."
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<abstract>Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (i) reliance on handcrafted features that limit generalizability, (ii) difficulty in capturing fine-grained traits like coherence and argumentation, and (iii) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose **EssayJudge**, the **first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits**. By leveraging MLLMs’ strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research. Our dataset and code will be available upon acceptance.</abstract>
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%0 Conference Proceedings
%T EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
%A Su, Jiamin
%A Yan, Yibo
%A Fu, Fangteng
%A Han, Zhang
%A Ye, Jingheng
%A Liu, Xiang
%A Huo, Jiahao
%A Zhou, Huiyu
%A Hu, Xuming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F su-etal-2025-essayjudge
%X Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (i) reliance on handcrafted features that limit generalizability, (ii) difficulty in capturing fine-grained traits like coherence and argumentation, and (iii) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose **EssayJudge**, the **first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits**. By leveraging MLLMs’ strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research. Our dataset and code will be available upon acceptance.
%R 10.18653/v1/2025.findings-acl.329
%U https://aclanthology.org/2025.findings-acl.329/
%U https://doi.org/10.18653/v1/2025.findings-acl.329
%P 6363-6389
Markdown (Informal)
[EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models](https://aclanthology.org/2025.findings-acl.329/) (Su et al., Findings 2025)
ACL
- Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, and Xuming Hu. 2025. EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6363–6389, Vienna, Austria. Association for Computational Linguistics.