Binjie Wang


2024

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OpenResearcher: Unleashing AI for Accelerated Scientific Research
Yuxiang Zheng | Shichao Sun | Lin Qiu | Dongyu Ru | Cheng Jiayang | Xuefeng Li | Jifan Lin | Binjie Wang | Yun Luo | Renjie Pan | Yang Xu | Qingkai Min | Zizhao Zhang | Yiwen Wang | Wenjie Li | Pengfei Liu
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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.