OpenResearcher: Unleashing AI for Accelerated Scientific Research
akhaliq
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.
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is there a live demo for this? I am unable to find one on the github page / paper, and don't have the time to set it up locally
Great, but how about the authors and their respective AI researchers start citing related work that also addressed this problem previously. In our paper, we propose and evaluate a framework that enables building complex workflows, one of which is writing a paper: https://arxiv.org/abs/2402.00854 Here is the benchmark with the paper generation workflow: https://github.com/ExtensityAI/benchmark/blob/main/src/evals/eval_computation_graphs.py#L551 Here are some samples: https://drive.google.com/drive/folders/1KZmWsos07xg9p6JEVgXi5YZJzG36GvrG?usp=sharing
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