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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.05276 |
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| _version_ | 1866915323204075520 |
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| author | Chu, Yucheng He, Peng Li, Hang Han, Haoyu Yang, Kaiqi Xue, Yu Li, Tingting Krajcik, Joseph Tang, Jiliang |
| author_facet | Chu, Yucheng He, Peng Li, Hang Han, Haoyu Yang, Kaiqi Xue, Yu Li, Tingting Krajcik, Joseph Tang, Jiliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation Chu, Yucheng He, Peng Li, Hang Han, Haoyu Yang, Kaiqi Xue, Yu Li, Tingting Krajcik, Joseph Tang, Jiliang Computation and Language 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. |
| title | Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.05276 |