SurveyAgent: A Conversational System for Personalized and Efficient Research Survey
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866917635156869120 |
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| author | Wang, Xintao Chen, Jiangjie Li, Nianqi Chen, Lida Yuan, Xinfeng Shi, Wei Ge, Xuyang Xu, Rui Xiao, Yanghua |
| author_facet | Wang, Xintao Chen, Jiangjie Li, Nianqi Chen, Lida Yuan, Xinfeng Shi, Wei Ge, Xuyang Xu, Rui Xiao, Yanghua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SurveyAgent: A Conversational System for Personalized and Efficient Research Survey Wang, Xintao Chen, Jiangjie Li, Nianqi Chen, Lida Yuan, Xinfeng Shi, Wei Ge, Xuyang Xu, Rui Xiao, Yanghua Computation and Language 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. |
| title | SurveyAgent: A Conversational System for Personalized and Efficient Research Survey |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.06364 |