从平面到结构：利用GraphRAG增强自动简答评分
From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG
AI总结：
本文提出GraphRAG框架，通过构建结构化知识图谱解决传统RAG在复杂教育内容中的不足，实验表明其在评估科学与工程实践方面表现更优。
摘要：
自动简答评分（ASAG）对扩大教育评估至关重要，但大语言模型（LLMs）因依赖通用预训练而常出现幻觉和严格评分标准不达标的问题。尽管检索增强生成（RAG）能缓解这些问题，但标准“平面”向量检索机制将知识视为孤立片段，无法捕捉结构关系和多跳推理，这对复杂教育内容至关重要。为此，我们引入GraphRAG框架，将参考材料组织成结构化知识图谱，显式建模概念间依赖关系。我们的方法采用双阶段流程：利用Microsoft GraphRAG进行高保真图构建，并使用HippoRAG神经符号算法执行关联图遍历，从而检索到完整的连接子图证据。在下一代科学标准（NGSS）数据集上的实验评估表明，这种结构方法在所有指标上均显著优于标准RAG基线。值得注意的是，HippoRAG实现对科学与工程实践（SEP）的评估有显著提升，证实了结构检索在验证更高阶学术评估所需逻辑推理链中的优越性。
英文摘要：
Automated short answer grading (ASAG) is critical for scaling educational assessment, yet large language models (LLMs) often struggle with hallucinations and strict rubric adherence due to their reliance on generalized pre-training. While Rretrieval-Augmented Generation (RAG) mitigates these issues, standard "flat" vector retrieval mechanisms treat knowledge as isolated fragments, failing to capture the structural relationships and multi-hop reasoning essential for complex educational content. To address this limitation, we introduce a Graph Retrieval-Augmented Generation (GraphRAG) framework that organizes reference materials into a structured knowledge graph to explicitly model dependencies between concepts. Our methodology employs a dual-phase pipeline: utilizing Microsoft GraphRAG for high-fidelity graph construction and the HippoRAG neurosymbolic algorithm to execute associative graph traversals, thereby retrieving comprehensive, connected subgraphs of evidence. Experimental evaluations on a Next Generation Science Standards (NGSS) dataset demonstrate that this structural approach significantly outperforms standard RAG baselines across all metrics. Notably, the HippoRAG implementation achieved substantial improvements in evaluating Science and Engineering Practices (SEP), confirming the superiority of structural retrieval in verifying the logical reasoning chains required for higher-order academic assessment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
