OpenResearcher: Unleashing AI for Accelerated Scientific Research
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866913565340860416 |
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| author | Zheng, Yuxiang Sun, Shichao Qiu, Lin Ru, Dongyu Jiayang, Cheng Li, Xuefeng Lin, Jifan Wang, Binjie Luo, Yun Pan, Renjie Xu, Yang Min, Qingkai Zhang, Zizhao Wang, Yiwen Li, Wenjie Liu, Pengfei |
| author_facet | Zheng, Yuxiang Sun, Shichao Qiu, Lin Ru, Dongyu Jiayang, Cheng Li, Xuefeng Lin, Jifan Wang, Binjie Luo, Yun Pan, Renjie Xu, Yang Min, Qingkai Zhang, Zizhao Wang, Yiwen Li, Wenjie Liu, Pengfei |
| contents | The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06941 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OpenResearcher: Unleashing AI for Accelerated Scientific Research Zheng, Yuxiang Sun, Shichao Qiu, Lin Ru, Dongyu Jiayang, Cheng Li, Xuefeng Lin, Jifan Wang, Binjie Luo, Yun Pan, Renjie Xu, Yang Min, Qingkai Zhang, Zizhao Wang, Yiwen Li, Wenjie Liu, Pengfei Information Retrieval The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher. |
| title | OpenResearcher: Unleashing AI for Accelerated Scientific Research |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2408.06941 |