title: SurveyX: Academic Survey Automation via Large Language Models
summary: 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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paper_authors.name: Xun Liang
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paper_authors.name: Jiawei Yang
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paper_authors.user: HaruTeru
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paper_authors.name: Chen Tang
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paper_authors.fullname: Travis Tang
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paper_authors.name: Zifan Zheng
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paper_authors.fullname: Zifan Zheng
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paper_authors.name: Simin Niu
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paper_authors.fullname: siminniu
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paper_authors.name: Shichao Song
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paper_authors.fullname: Shichao Song
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paper_authors.user: Ki-Seki
paper_authors._id: 67bbdb46d94d32bcfba70dbe
paper_authors.name: Hanyu Wang
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paper_authors.fullname: hanyu Wang
paper_authors.type: user
paper_authors.user: UglyToilet
paper_authors._id: 67bbdb46d94d32bcfba70dbf
paper_authors.name: Bo Tang
paper_authors._id: 67bbdb46d94d32bcfba70dc0
paper_authors.name: Feiyu Xiong
paper_authors._id: 67bbdb46d94d32bcfba70dc1
paper_authors.name: Keming Mao
paper_authors._id: 67bbdb46d94d32bcfba70dc2
paper_authors.name: Zhiyu li
paper_title: SurveyX: Academic Survey Automation via Large Language Models
paper_summary: 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
paper_ai_summary: SurveyX enhances automated survey generation through innovative preprocessing and post-processing methods, improving content and citation quality beyond existing systems.
paper_ai_keywords: Large Language Models LLMs automated survey generation context window AttributeTree reference retrieval re-polishing process content quality citation quality human expert performance