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
2024-08-14
Summary
This paper introduces OpenResearcher, a new platform that uses AI to help researchers quickly find and understand scientific information, making it easier for them to keep up with advancements in their fields.
What's the problem?
Researchers face challenges in staying updated with the rapidly growing amount of scientific literature. This makes it difficult for them to explore new topics and find relevant information efficiently.
What's the solution?
OpenResearcher leverages Artificial Intelligence techniques to answer researchers' questions and provide access to the latest, domain-specific knowledge. It uses a method called Retrieval-Augmented Generation (RAG) to combine large language models with current scientific information. The platform includes tools that help understand queries, search literature, filter results, and refine answers, allowing researchers to save time and discover new insights.
Why it matters?
This research is important because it addresses the overwhelming amount of information in science today. By providing a tool that helps researchers navigate this information more effectively, OpenResearcher can accelerate scientific discovery and innovation, ultimately leading to breakthroughs that benefit society.
Abstract
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.