Improve LLM-based Automatic Essay Scoring with Linguistic Features

• URL: http://arxiv.org/abs/2502.09497v1
• Date: Thu, 13 Feb 2025 17:09:52 GMT
• Title: Improve LLM-based Automatic Essay Scoring with Linguistic Features
• Authors: Zhaoyi Joey Hou, Alejandro Ciuba, Xiang Lorraine Li,
• Abstract summary: This paper develops a scoring system capable of handling essays across diverse prompts. Existing methods typically fall into two categories: supervised feature-based approaches and large language model (LLM)-based methods.
• Score: 46.41475844992872
• License:
• Abstract: 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.

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