Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading

• URL: http://arxiv.org/abs/2309.09338v1
• Date: Sun, 17 Sep 2023 18:04:34 GMT
• Title: Performance of the Pre-Trained Large Language Model GPT-4 on Automated Short Answer Grading
• Authors: Gerd Kortemeyer
• Abstract summary: We studied the performance of GPT-4 on the standard benchmark 2-way and 3-way datasets SciEntsBank and Beetle. We found that the performance of the pre-trained general-purpose GPT-4 LLM is comparable to hand-engineered models, but worse than pre-trained LLMs that had specialized training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated Short Answer Grading (ASAG) has been an active area of machine-learning research for over a decade. It promises to let educators grade and give feedback on free-form responses in large-enrollment courses in spite of limited availability of human graders. Over the years, carefully trained models have achieved increasingly higher levels of performance. More recently, pre-trained Large Language Models (LLMs) emerged as a commodity, and an intriguing question is how a general-purpose tool without additional training compares to specialized models. We studied the performance of GPT-4 on the standard benchmark 2-way and 3-way datasets SciEntsBank and Beetle, where in addition to the standard task of grading the alignment of the student answer with a reference answer, we also investigated withholding the reference answer. We found that overall, the performance of the pre-trained general-purpose GPT-4 LLM is comparable to hand-engineered models, but worse than pre-trained LLMs that had specialized training.

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