OpenResearcher: Unleashing AI for Accelerated Scientific Research
Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang
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.
BibTeX
@inproceedings{zheng-etal-2024-openresearcher,
title = "{O}pen{R}esearcher: Unleashing {AI} for Accelerated Scientific Research",
author = "Zheng, Yuxiang and
Sun, Shichao and
Qiu, Lin and
Ru, Dongyu and
Jiayang, Cheng and
Li, Xuefeng and
Lin, Jifan and
Wang, Binjie and
Luo, Yun and
Pan, Renjie and
Xu, Yang and
Min, Qingkai and
Zhang, Zizhao and
Wang, Yiwen and
Li, Wenjie and
Liu, Pengfei",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.22/",
doi = "10.18653/v1/2024.emnlp-demo.22",
pages = "209--218"
}