Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2504.05276v1
• Date: Mon, 07 Apr 2025 17:17:41 GMT
• Title: Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation
• Authors: Yucheng Chu, Peng He, Hang Li, Haoyu Han, Kaiqi Yang, Yu Xue, Tingting Li, Joseph Krajcik, Jiliang Tang,
• Abstract summary: Large language models (LLMs) possess human-like ability in linguistic tasks.<n>Retrieval-augmented generation (RAG) emerges as a promising solution.<n>We propose an adaptive RAG framework for automated grading.
• Score: 32.12573291200363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that possess human-like ability in linguistic tasks are increasingly popular in assisting human graders to reduce their workload. However, LLMs' limitations in domain knowledge restrict their understanding in task-specific requirements and hinder their ability to achieve satisfactory performance. Retrieval-augmented generation (RAG) emerges as a promising solution by enabling LLMs to access relevant domain-specific knowledge during assessment. In this work, we propose an adaptive RAG framework for automated grading that dynamically retrieves and incorporates domain-specific knowledge based on the question and student answer context. Our approach combines semantic search and curated educational sources to retrieve valuable reference materials. Experimental results in a science education dataset demonstrate that our system achieves an improvement in grading accuracy compared to baseline LLM approaches. The findings suggest that RAG-enhanced grading systems can serve as reliable support with efficient performance gains.

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