用大模型生成知识图谱分解物理题，让子问题更贴合原题逻辑。
Knowledge Graphs are all you need: Leveraging KGs in Physics Question Answering
- 用大模型构建知识图谱，提取题目内在逻辑
- 子问题与原题逻辑一致性显著提升
- 适合教育AI、智能辅导系统开发者参考
本研究探讨利用大语言模型生成的知识图谱来分解中学物理问题的有效性。提出一种增强问答任务响应质量的流程：先由大模型构建捕捉问题内部逻辑的知识图谱，再据此生成子问题。假设该方法生成的子问题相比传统分解技术更具逻辑一致性。实验结果表明，基于知识图谱生成的子问题在逻辑保真度上显著优于传统方法。该方法不仅提升了学习体验，使子问题更清晰、上下文相关，还凸显了大模型在革新教育方法方面的潜力。研究结果表明，将AI应用于提升教育内容质量与效率具有广阔前景。
原文摘要 · Abstract (English)
This study explores the effectiveness of using knowledge graphs generated by large language models to decompose high school-level physics questions into sub-questions. We introduce a pipeline aimed at enhancing model response quality for Question Answering tasks. By employing LLMs to construct knowledge graphs that capture the internal logic of the questions, these graphs then guide the generation of subquestions. We hypothesize that this method yields sub-questions that are more logically consistent with the original questions compared to traditional decomposition techniques. Our results show that sub-questions derived from knowledge graphs exhibit significantly improved fidelity to the original question's logic. This approach not only enhances the learning experience by providing clearer and more contextually appropriate sub-questions but also highlights the potential of LLMs to transform educational methodologies. The findings indicate a promising direction for applying AI to improve the quality and effectiveness of educational content.
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