用智能代理自动构建知识图谱，生成科研文献综述。
Mixture of Knowledge Minigraph Agents for Literature Review Generation
- 设计知识图谱构建代理，从论文中提取概念关系。
- 多路径摘要代理从不同视角整合信息生成综述段落。
- 在三个数据集上验证有效，适合科研人员快速写综述。
文献综述在科学研究中至关重要，有助于理解研究现状、发现空白并指导未来方向。然而，全面开展文献综述耗时较长。本文提出一种新框架——协同知识小图代理（CKMAs），用于自动化学术文献综述。设计了基于提示的算法知识小图构建代理（KMCA），可识别学术文献中概念间的关系，并自动生成知识小图。利用大语言模型对构建的知识小图进行处理，多路径摘要代理（MPSA）能从不同视角高效组织概念与关系，生成文献综述段落。我们在三个基准数据集上评估了CKMAs，实验结果表明该方法有效，进一步揭示了大语言模型在科学研究中的广阔应用前景。
原文摘要 · Abstract (English)
Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research.
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