SurveyAgent: A Conversational System for Personalized and Efficient Research Survey
<|reference_start|>SurveyAgent: A Conversational System for Personalized and Efficient Research Survey: 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.<|reference_end|>
@article{wang2024surveyagent:,
    title={SurveyAgent: A Conversational System for Personalized and Efficient
  Research Survey},
    author={Xintao Wang, Jiangjie Chen, Nianqi Li, Lida Chen, Xinfeng Yuan, Wei
  Shi, Xuyang Ge, Rui Xu, Yanghua Xiao},
    journal={arXiv preprint arXiv:2404.06364},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06364},
    primaryClass={cs.CL}
}