Vision-language models reach 98.4% accuracy on 3141 handwritten single-letter exam answers across 61 tests, with false-negative rate reduced to 0.58% via reference-solution prompting.
arXiv preprint arXiv:2402.15307 , year =
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Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models
Vision-language models reach 98.4% accuracy on 3141 handwritten single-letter exam answers across 61 tests, with false-negative rate reduced to 0.58% via reference-solution prompting.