OpenResearcher: Unleashing AI for Accelerated Scientific Research
Authors: name: Yuxiang Zheng, name: Shichao Sun, name: Lin Qiu, name: Dongyu Ru, name: Cheng Jiayang, name: Xuefeng Li, name: Jifan Lin, name: Binjie Wang, name: Yun Luo, name: Renjie Pan, name: Yang Xu, name: Qingkai Min, name: Zizhao Zhang, name: Yiwen Wang, name: Wenjie Li, name: Pengfei Liu

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.