{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ai2-scholar-qa-organized-literature-synthesis","title":"Ai2 Scholar QA: Organized Literature Synthesis with Attribution","arxiv_id":"2504.10861","date":"2025-04-15","proceeding":null,"authors":["Amanpreet Singh","Joseph Chee Chang","Chloe Anastasiades","Dany Haddad","Aakanksha Naik","Amber Tanaka","Angele Zamarron","Cecile Nguyen","Jena D. Hwang","Jason Dunkleberger","Matt Latzke","Smita Rao","Jaron Lochner","Rob Evans","Rodney Kinney","Daniel S. Weld","Doug Downey","Sergey Feldman"],"abstract":"Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source Python package and interactive web app, along with paper indexes accessible through public APIs and downloadable datasets. We describe our system in detail and present experiments analyzing its key design decisions. 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