SurveyAgent: A Conversational System for Personalized and Efficient Research Survey (2404.06364v1)
Abstract: In the rapidly advancing research fields such as AI, managing and staying abreast of the latest scientific literature has become a significant challenge for researchers. Although previous efforts have leveraged AI to assist with literature searches, paper recommendations, and question-answering, a comprehensive support system that addresses the holistic needs of researchers has been lacking. This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. This system stands out by offering a unified platform that supports researchers through various stages of their literature review process, facilitated by a conversational interface that prioritizes user interaction and personalization. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
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- Xintao Wang (132 papers)
- Jiangjie Chen (46 papers)
- Nianqi Li (4 papers)
- Lida Chen (8 papers)
- Xinfeng Yuan (6 papers)
- Wei Shi (116 papers)
- Xuyang Ge (9 papers)
- Rui Xu (199 papers)
- Yanghua Xiao (151 papers)