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Corrected Text:
A new artificial intelligence system called OpenScholar, developed by researchers at the University of Washington (UW) and the Allen Institute for AI (Ai2), is designed to help scientists navigate the ever-growing volume of research papers.OpenScholar outperformed all the systems it was tested against. The team had 16 scientists review answers from the models and compare them with human-written responses. The scientists preferred OpenScholar answers to human answers 51% of the time, but when they combined OpenScholar citation methods and pipelines with GPT-4o (a much bigger model), the scientists preferred the AI-written answers to human answers 70% of the time. They picked answers from GPT-4o on its own only 32% of the time.
“Scientists see so many papers coming out every day that it’s impossible to keep up,” Asai said. “But the existing AI systems weren’t designed for scientists’ specific needs. We’ve already seen a lot of scientists using OpenScholar and as it’s open-source, others are building on this research and already improving on our results. we’re working on a followup model, DR Tulu,which builds on OpenScholar’s findings and performs multi-step search and information gathering to produce more extensive responses.”
Othre co-authors include Jacqueline He, Rulin Shao, Weijia Shi, all UW doctoral students in the Allen School; Dan Weld, a UW professor emeritus in the Allen School and general manager and chief scientist at Ai2; Varsha kishore, a UW postdoc in the Allen School and postdoc at Ai2; Luke Zettlemoyer, a UW professor in the Allen School; Pang Wei Koh, a UW assistant professor in the Allen School; Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D’Arcy, David Wadden, matt Latzke, Jenna Sparks and Jena D.Hwang of Ai2; Wen-
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