SurveyAgent: A Conversational System for Personalized and Efficient Research Survey
arxiv(2024)
摘要
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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