A Survey Forest Diagram : Gain a Divergent Insight View on a Specific Research Topic

Jinghong Li, Wen Gu, Koichi Ota, Shinobu Hasegawa
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Abstract

With the exponential growth in the number of papers and the trend of AI research, the use of Generative AI for information retrieval and question-answering has become popular for conducting research surveys. However, novice researchers unfamiliar with a particular field may not significantly improve their efficiency in interacting with Generative AI because they have not developed divergent thinking in that field. This study aims to develop an in-depth Survey Forest Diagram that guides novice researchers in divergent thinking about the research topic by indicating the citation clues among multiple papers, to help expand the survey perspective for novice researchers.
调查森林图 :获得关于特定研究课题的发散性洞察视图
随着论文数量的指数级增长和人工智能研究的发展趋势,使用生成式人工智能进行信息检索和问题解答已成为开展研究调查的流行方式。然而,不熟悉特定领域的新手研究人员可能无法显著提高与生成式人工智能交互的效率,因为他们尚未在该领域形成发散思维。本研究旨在开发一种深度调查森林图(Survey Forest Diagram),通过指出多篇论文之间的引用线索,引导新手研究人员对研究主题进行发散性思考,从而帮助新手研究人员拓展调查视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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