用大模型自动写学术综述，质量接近人工专家。
SurveyX: Academic Survey Automation via Large Language Models
- 分准备与生成两阶段，结合检索与树状属性结构提升组织性
- 内容质量提升0.259，引用质量提高1.76，逼近人工水平
- 适合需要快速生成高质量综述的研究者使用
大型语言模型（LLMs）展现出卓越的理解能力与广博知识，提示其可作为自动化综述生成的高效工具。然而，现有自动化综述生成研究受限于上下文窗口有限、内容深度不足及缺乏系统评估框架等关键问题。受人类写作过程启发，我们提出SurveyX，一种高效且结构化的自动化综述生成系统，将综述撰写过程分解为准备与生成两个阶段。通过创新引入在线参考检索、名为AttributeTree的预处理方法以及重润色流程，SurveyX显著提升了综述生成效率。实验结果表明，SurveyX在内容质量（提升0.259）和引用质量（提升1.76）上优于现有系统，在多个评估维度上接近人工专家水平。SurveyX生成的示例综述可在www.surveyx.cn查看。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation. However, recent research related to automated survey generation remains constrained by some critical limitations like finite context window, lack of in-depth content discussion, and absence of systematic evaluation frameworks. Inspired by human writing processes, we propose SurveyX, an efficient and organized system for automated survey generation that decomposes the survey composing process into two phases: the Preparation and Generation phases. By innovatively introducing online reference retrieval, a pre-processing method called AttributeTree, and a re-polishing process, SurveyX significantly enhances the efficacy of survey composition. Experimental evaluation results show that SurveyX outperforms existing automated survey generation systems in content quality (0.259 improvement) and citation quality (1.76 enhancement), approaching human expert performance across multiple evaluation dimensions. Examples of surveys generated by SurveyX are available on www.surveyx.cn
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