SurveyX: Academic Survey Automation via Large Language Models
Authors: name: Xun Liang, name: Jiawei Yang, name: Yezhaohui Wang, name: Chen Tang, name: Zifan Zheng, name: Simin Niu, name: Shichao Song, name: Hanyu Wang, name: Bo Tang, name: Feiyu Xiong, name: Keming Mao, name: Zhiyu li

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