Improve LLM-based Automatic Essay Scoring with Linguistic Features

Fuente: arXiv
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Auteurs principaux: Hou, Zhaoyi Joey, Ciuba, Alejandro, Li, Xiang Lorraine
Format: Preprint
Publié: 2025
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author Hou, Zhaoyi Joey
Ciuba, Alejandro
Li, Xiang Lorraine
author_facet Hou, Zhaoyi Joey
Ciuba, Alejandro
Li, Xiang Lorraine
contents Automatic Essay Scoring (AES) assigns scores to student essays, reducing the grading workload for instructors. Developing a scoring system capable of handling essays across diverse prompts is challenging due to the flexibility and diverse nature of the writing task. Existing methods typically fall into two categories: supervised feature-based approaches and large language model (LLM)-based methods. Supervised feature-based approaches often achieve higher performance but require resource-intensive training. In contrast, LLM-based methods are computationally efficient during inference but tend to suffer from lower performance. This paper combines these approaches by incorporating linguistic features into LLM-based scoring. Experimental results show that this hybrid method outperforms baseline models for both in-domain and out-of-domain writing prompts.
format Preprint
id arxiv_https___arxiv_org_abs_2502_09497
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Improve LLM-based Automatic Essay Scoring with Linguistic Features
Hou, Zhaoyi Joey
Ciuba, Alejandro
Li, Xiang Lorraine
Computation and Language
Artificial Intelligence
Automatic Essay Scoring (AES) assigns scores to student essays, reducing the grading workload for instructors. Developing a scoring system capable of handling essays across diverse prompts is challenging due to the flexibility and diverse nature of the writing task. Existing methods typically fall into two categories: supervised feature-based approaches and large language model (LLM)-based methods. Supervised feature-based approaches often achieve higher performance but require resource-intensive training. In contrast, LLM-based methods are computationally efficient during inference but tend to suffer from lower performance. This paper combines these approaches by incorporating linguistic features into LLM-based scoring. Experimental results show that this hybrid method outperforms baseline models for both in-domain and out-of-domain writing prompts.
title Improve LLM-based Automatic Essay Scoring with Linguistic Features
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2502.09497