大模型
arxiv.org ·
利用 LLM 与知识图谱引导推理对学生意义建构进行建模
该 arXiv 论文研究指令微调 LLM 能否在无需任务特定训练的情况下，对协作科学学习中的学生意义建构进行多维分析，并检验结构化知识状态信息是否改善模型推理。作者在两个中等规模 LLM 上，对 23 个专家标注的对话片段，在定义性提示、推理模式和轮次结构等不同条件下进行评估。结果显示：无推理时模型倾向高估成功意义建构，启用推理的提示能更好识别不成功案例；知识状态诊断可提供额外依据，提升对不成功意义建构的检测并与专家标注更一致。没有单一配置在所有维度上最优，说明该任务具有多维性。
AI推荐理由 arXiv 论文，摘要明确给出实验设置：两个中等规模 LLM、23 个专家标注片段、多种提示条件，并报告了推理提示与知识状态诊断对识别不成功意义建构的改进，属于可验证的学术证据。局限在于样本量小（23 个片段）、仅评估两个模型，且未给出具体模型名称与量化指标，应用面偏教育分析，对通用 AI/Agent 工程价值有限。
62
正文
Computer Science > Computation and Language
arXiv:2609.31046 (cs)
[Submitted on 25 Sep 2026]
Title:Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference
Authors:Özge Alacam, Zübeyde Demet Kirbulut Güneş, Funda Ekici, Nurcan Turan-Oluk, Dilay Dinçdemir, Hakkı Kadayıfçı, Sevinç Nihal Yeşiloğlu, Burcu Işık, Halil Tümay, Sinem Gencer
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Abstract:Collaborative science learning requires nuanced interpretation of student dialogue to characterize how learners identify knowledge gaps, build explanations, and work toward resolution - a theory-driven analysis that is labor-intensive and difficult to scale. We investigate whether instruction-tuned large language models (LLMs) can support multidimensional analysis of collaborative sensemaking without task-specific training, and whether structured knowledge-state information improves model inference. We evaluate two mid-size LLMs on 23 richly annotated, expert-labeled episodes across prompting conditions that vary definitional scaffolding, reasoning mode, and turn structure. Without reasoning, models tend to overpredict successful sensemaking; reasoning-enabled prompting improves identification of unsuccessful cases. Knowledge-state diagnostics provide additional grounding, improving detection of unsuccessful sensemaking and increasing agreement with expert annotations. No single configuration performs best across all sensemaking dimensions, underscoring the multidimensional nature of the task.
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Computation and Language (cs.CL)
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arXiv:2609.31046 [cs.CL]
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arXiv:2609.31046v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.31046
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From: Özge Alacam [view email]
[v1]
Fri, 25 Sep 2026 09:41:27 UTC (1,267 KB)
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