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AutoSurvey: Large Language Models Can Automatically Write Surveys

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arxiv 2406.10252 v2 pith:EROTVZ6O submitted 2024-06-10 cs.IR cs.AIcs.CL

Yidong Wang , Qi Guo , Wenjin Yao , Hongbo Zhang , Xin Zhang , Zhen Wu , Meishan Zhang , Xinyu Dai
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Min Zhang Qingsong Wen Wei Ye Shikun Zhang Yue Zhang
This is my paper · ORCID
classification cs.IRcs.AIcs.CL
keywords autosurveychallengesevaluationsurveyautomatingcomprehensivecreationlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due to the vast volume and complexity of information, prompting the need for efficient survey methods. While large language models (LLMs) offer promise in automating this process, challenges such as context window limitations, parametric knowledge constraints, and the lack of evaluation benchmarks remain. AutoSurvey addresses these challenges through a systematic approach that involves initial retrieval and outline generation, subsection drafting by specialized LLMs, integration and refinement, and rigorous evaluation and iteration. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.We open our resources at \url{https://github.com/AutoSurveys/AutoSurvey}.

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Cited by 5 Pith papers

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  4. Automated Capability Discovery via Foundation Model Self-Exploration

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