研究人员开发了一种使用微调的 BART Transformer 模型自动评分入门级 C++ 编程作业的方法。该方法结合了基于评分标准的标准和多任务学习，以更好地模仿人类教师的评分行为。实验表明，与标准方法相比，这种基于评分标准的指导训练，特别是使用基于边界的软标签，实现了更低的错误率并改善了分数分布的一致性。 AI
影响 这项研究可能为编程课程带来更准确且与教师评分一致的自动化评分系统。
排序理由 该集群包含一篇研究论文，详细介绍了一种使用 Transformer 模型进行自动化评分的新方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2606.03814v1 Announce Type: new Abstract: This paper investigates rubric-aware, multitask fine-tuning of transformer models for automated grading of introductory C++ programming assignments, with the goal of producing grade predictions that better reflect instructor grading…
This paper investigates rubric-aware, multitask fine-tuning of transformer models for automated grading of introductory C++ programming assignments, with the goal of producing grade predictions that better reflect instructor grading behavior than general-purpose LLMs. Using multi…
This paper investigates rubric-aware, multitask fine-tuning of transformer models for automated grading of introductory C++ programming assignments, with the goal of producing grade predictions that better reflect instructor grading behavior than general-purpose LLMs. Using multi…