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English(EN) K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs

新的K-12知识图谱基准测试大型语言模型课程认知

研究人员开发了K12-KGraph,一个新颖的知识图谱,旨在专门评估和训练K-12教育领域的大型语言模型(LLMs)。该图谱源自官方教材,捕捉了课程结构,包括先决条件和概念关系,超越了简单的事实回忆。为了支持这一点,他们创建了K12-Bench(一个包含23,640个问题的基准测试集)和K12-Train(一个微调数据集)。实验表明,当前的大型语言模型在课程认知方面存在困难,而K12-Train数据集在教育基准测试上显著提高了性能,且样本效率高。 AI

影响 为评估大型语言模型对教育课程的理解能力建立了新的基准,可能推动更具教学意识的人工智能的发展。

排序理由 该集群描述了一篇介绍用于评估教育领域大型语言模型的新颖数据集和基准测试的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的K-12知识图谱基准测试大型语言模型课程认知

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该集群描述了一篇介绍用于评估教育领域大型语言模型的新颖数据集和基准测试的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Wentao Zhang ·

    K12-KGraph:一个与课程对齐的知识图谱,用于教育领域大型语言模型的基准测试和训练

    Large language models (LLMs) are increasingly used in K-12 education, yet existing benchmarks such as C-Eval, CMMLU, GaokaoBench, and EduEval mainly evaluate factual recall through exam-style question answering. Effective educational AI additionally requires curriculum cognition:…