@inproceedings{singh-etal-2025-ai2,
    title = "Ai2 Scholar {QA}: Organized Literature Synthesis with Attribution",
    author = "Singh, Amanpreet  and
      Chang, Joseph Chee  and
      Haddad, Dany  and
      Naik, Aakanksha  and
      Hwang, Jena D.  and
      Kinney, Rodney  and
      Weld, Daniel S  and
      Downey, Doug  and
      Feldman, Sergey",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.49/",
    pages = "513--523",
    ISBN = "979-8-89176-253-4",
    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. In an evaluation on a recent scientific QA benchmark, we find that Ai2 Scholar QA outperforms competing systems."
}