AutoSurvey: Large Language Models Can Automatically Write Surveys
This addresses the challenge of efficiently producing surveys for researchers and practitioners in rapidly evolving domains, though it appears incremental as it builds on existing LLM capabilities.
The paper tackles the problem of automating literature survey creation in fast-moving fields like AI by introducing AutoSurvey, a method that uses large language models to generate comprehensive surveys, with experimental validation showing its effectiveness.
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}.