@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://aclanthology.org/2025.acl-demo.49/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Ai2 Scholar QA: Organized Literature Synthesis with Attribution
%A Singh, Amanpreet
%A Chang, Joseph Chee
%A Haddad, Dany
%A Naik, Aakanksha
%A Hwang, Jena D.
%A Kinney, Rodney
%A Weld, Daniel S.
%A Downey, Doug
%A Feldman, Sergey
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F singh-etal-2025-ai2
%X 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.
%R 10.18653/v1/2025.acl-demo.49
%U https://aclanthology.org/2025.acl-demo.49/
%U https://doi.org/10.18653/v1/2025.acl-demo.49
%P 513-523
Markdown (Informal)
[Ai2 Scholar QA: Organized Literature Synthesis with Attribution](https://aclanthology.org/2025.acl-demo.49/) (Singh et al., ACL 2025)
ACL
- Amanpreet Singh, Joseph Chee Chang, Dany Haddad, Aakanksha Naik, Jena D. Hwang, Rodney Kinney, Daniel S Weld, Doug Downey, and Sergey Feldman. 2025. Ai2 Scholar QA: Organized Literature Synthesis with Attribution. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 513–523, Vienna, Austria. Association for Computational Linguistics.