@inproceedings{haihong-etal-2024-assessing,
title = "Assessing Essay Fluency with Large Language Models",
author = "Haihong, Wu and
Chang, Ao and
Shiwen, Ni",
editor = "Lin, Hongfei and
Tan, Hongye and
Li, Bin",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-3.29/",
pages = "262--268",
language = "eng",
abstract = "{\textquotedblleft}With the development of education and the widespread use of the internet, the scale of essay evaluation has increased, making the cost and efficiency of manual grading a significant challenge. To address this, The Twenty-third China National Conference on Computational Linguistics (CCL2024) established evaluation contest for essay fluency. This competition has three tracks corresponding to three sub-tasks. This paper conducts a detailed analysis of different tasks,employing the BERT model as well as the latest popular large language models Qwen to address these sub-tasks. As a result, our overall scores for the three tasks reached 37.26, 42.48, and 47.64.{\textquotedblright}"
}
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<abstract>“With the development of education and the widespread use of the internet, the scale of essay evaluation has increased, making the cost and efficiency of manual grading a significant challenge. To address this, The Twenty-third China National Conference on Computational Linguistics (CCL2024) established evaluation contest for essay fluency. This competition has three tracks corresponding to three sub-tasks. This paper conducts a detailed analysis of different tasks,employing the BERT model as well as the latest popular large language models Qwen to address these sub-tasks. As a result, our overall scores for the three tasks reached 37.26, 42.48, and 47.64.”</abstract>
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%0 Conference Proceedings
%T Assessing Essay Fluency with Large Language Models
%A Haihong, Wu
%A Chang, Ao
%A Shiwen, Ni
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F haihong-etal-2024-assessing
%X “With the development of education and the widespread use of the internet, the scale of essay evaluation has increased, making the cost and efficiency of manual grading a significant challenge. To address this, The Twenty-third China National Conference on Computational Linguistics (CCL2024) established evaluation contest for essay fluency. This competition has three tracks corresponding to three sub-tasks. This paper conducts a detailed analysis of different tasks,employing the BERT model as well as the latest popular large language models Qwen to address these sub-tasks. As a result, our overall scores for the three tasks reached 37.26, 42.48, and 47.64.”
%U https://aclanthology.org/2024.ccl-3.29/
%P 262-268
Markdown (Informal)
[Assessing Essay Fluency with Large Language Models](https://aclanthology.org/2024.ccl-3.29/) (Haihong et al., CCL 2024)
ACL
- Wu Haihong, Ao Chang, and Ni Shiwen. 2024. Assessing Essay Fluency with Large Language Models. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 262–268, Taiyuan, China. Chinese Information Processing Society of China.