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Wen-tau Yih

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Articles

Synthesizing scientific literature with retrieval-augmented language models

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By Akari Asai, Jacqueline Jialu He, Rulin Shao, Weijia Shi, Amanpreet Singh Verified, Joseph Chang Verified, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D’Arcy, David Wadden, Matt Latzke, Jena D. Hwang, Varsha Kishore, Minyang Tian, Pan Ji, Shengyan Liu, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Daniel S. Weld, Doug B. Downey, Wen-tau Yih, Pang Wei Koh, Hannaneh Hajishirzi
| Flipboard
9 hours ago After a year of headlines claiming AI would replace millions of jobs, companies are quietly discovering something else instead. AI isn’t eliminating human work. It’s redistributing human judgment, away from routine tasks and into the narrow zones where ambiguity is high, mistakes are costly, and …
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Synthesizing scientific literature with retrieval-augmented language models - Nature

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By Akari Asai, Jacqueline Jialu He, Rulin Shao, Weijia Shi, Amanpreet Singh Verified, Joseph Chang Verified, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D’Arcy, David Wadden, Matt Latzke, Jena D. Hwang, Varsha Kishore, Minyang Tian, Pan Ji, Shengyan Liu, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Daniel S. Weld, Doug B. Downey, Wen-tau Yih, Pang Wei Koh, Hannaneh Hajishirzi
| Nature Verified
Abstract Scientific progress depends on the ability of researchers to synthesize the growing body of literature. Can large language models (LLMs) assist scientists in this task? Here we introduce OpenScholar, a specialized retrieval-augmented language model (LM)1 that answers scientific queries by identifying relevant passages from 45 million open-access papers and synthesizing citation-backed responses.
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[2508.09494] Learning Facts at Scale with Active Reading

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By Vincent-Pierre Berges, Xilun Chen, Wen-tau Yih, Gargi Ghosh
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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