SurveyX：基于大语言模型的学术调查自动化系统

摘要

大型语言模型（LLMs）展现了卓越的理解能力与庞大的知识储备，表明其可作为自动化问卷生成的高效工具。然而，近期关于自动化问卷生成的研究仍受限于一些关键问题，如有限的上下文窗口、缺乏深入内容探讨以及系统化评估框架的缺失。受人类写作流程启发，我们提出了SurveyX，一个高效且结构化的自动化问卷生成系统，将问卷编制过程分解为准备与生成两个阶段。通过创新性地引入在线参考文献检索、名为AttributeTree的预处理方法及再润色流程，SurveyX显著提升了问卷编制的效能。实验评估结果显示，SurveyX在内容质量（提升0.259）和引用质量（提升1.76）上均优于现有自动化问卷生成系统，在多个评估维度上接近人类专家水平。SurveyX生成的问卷示例可在www.surveyx.cn查阅。

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

SurveyX：基于大语言模型的学术调查自动化系统

SurveyX: Academic Survey Automation via Large Language Models

摘要

Summary

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