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 relationships between information pieces 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 information pieces and relationships from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results demonstrate that the proposed techniques generate informative, complete, consistent, and insightful summaries for different research problems, promoting the use of LLMs in more professional fields.

翻译：文献综述在科学研究中发挥着至关重要的作用，它有助于理解特定主题的研究现状、识别研究空白并指导未来研究。然而，进行全面文献综述的过程仍然耗时。本文提出了一种新颖的框架——协作知识微图智能体（CKMAs），以实现学术文献综述的自动化。我们设计了一种新颖的基于提示的算法，即知识微图构建智能体（KMCA），用于识别学术文献中信息片段之间的关系，并自动构建知识微图。通过利用大语言模型在已构建知识微图上的能力，多路径摘要智能体（MPSA）能够从不同视角高效地组织信息片段及其关系，以生成文献综述段落。我们在三个基准数据集上评估了CKMAs。实验结果表明，所提出的技术能为不同的研究问题生成信息丰富、完整、一致且富有洞察力的摘要，推动了大语言模型在更专业领域的应用。