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English(EN) Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue

LLM共识可能误导对学生数学错误的诊断

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究探讨了大型语言模型（LLMs）在诊断K-12数学辅导对话中学生失败模式的可靠性。研究人员发现，尽管LLMs与人类编码员之间表现出中等程度的一致性，但它们彼此之间的一致性却显著更高。这表明LLMs之间的一致性可能产生一种虚假的有效性感，突显了在学习分析中需要独立证据来确认模型生成解释的准确性。 AI

影响 强调了在教育数据分析中使用LLM共识作为有效性代理时需要谨慎。

排序理由 该集群包含一篇详细介绍LLM能力研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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LLM共识可能误导对学生数学错误的诊断

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该集群包含一篇详细介绍LLM能力研究结果的学术论文。[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) · Clayton Cohn, Joyce Fonteles, Kirk Vanacore, Gianni Mazza, Candida Crawford, Tom Hooper, Gautam Biswas, Rene Kizilcec ·

    协议并非有效性：跨模型LLM在诊断K-12数学辅导对话中学生失败模式方面的一致性

    arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning analytics research increasingly relies on large language models (LLMs) to extract…

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