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Automated Short Answer Grading (ASAG) has been an active area ofmachine-learning research for over a decade. It promises to let educators gradeand give feedback on free-form responses in large-enrollment courses in spiteof limited availability of human graders. Over the years, carefully trainedmodels have achieved increasingly higher levels of performance. More recently,pre-trained Large Language Models (LLMs) emerged as a commodity, and anintriguing question is how a general-purpose tool without additional trainingcompares to specialized models. We studied the performance of GPT-4 on thestandard benchmark 2-way and 3-way datasets SciEntsBank and Beetle, where inaddition to the standard task of grading the alignment of the student answerwith a reference answer, we also investigated withholding the reference answer.We found that overall, the performance of the pre-trained general-purpose GPT-4LLM is comparable to hand-engineered models, but worse than pre-trained LLMsthat had specialized training.

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