Ai2 OpenScholar: Scientific literature synthesis with retrieval-augmented language models
« Scientific progress hinges on our ability to find, synthesize, and build on relevant knowledge from the scientific literature. However, the exponential growth of this literature—with millions of papers now published each year—has made it increasingly difficult for scientists to find the information they need or even stay abreast of the latest findings in a single subfield.
To help scientists effectively navigate and synthesize scientific literature, we introduce Ai2 OpenScholar—a collaborative effort between the University of Washington and the Allen Institute for AI. OpenScholar is a retrieval-augmented language model (LM) designed to answer user queries by first searching for relevant papers in the literature and then generating responses grounded in those sources. (…) »
