Cited by 19Topic ModelingMeta-analysis and systematic reviewsBiomedical Text Mining and Ontologies

The DOI matches its Crossref record; 5 of the 6 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 28 sources its claims cite is checked for support yet.

Ai2 Scholar QA: Organized Literature Synthesis with Attribution

Open access
Singh, Amanpreet, Chang, Joseph Chee, Haddad, Dany, Naik, Aakanksha, Hwang, Jena D., Kinney, Rodney, Weld, Daniel S, Downey, Doug, Feldman, Sergey·2025·Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Abstract

Amanpreet Singh, Joseph Chee Chang, Dany Haddad, Aakanksha Naik, Jena D. Hwang, Rodney Kinney, Daniel S Weld, Doug Downey, Sergey Feldman. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). 2025.

Checked 2026-11-10 · 1 reference not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Ai2 Scholar QA is a free, open-source scientific question-answering system that retrieves from Semantic Scholar abstracts and a new 11.7M-paper full-text index, re-ranks passages, and uses multi-step LLM generation to produce cited, organized reports. It reports state-of-the-art results on the ScholarQA-CS benchmark and positive real-world usage feedback.

Key findings

Ai2 Scholar QA outperforms competing systems on ScholarQA-CS, with its Claude 3.7 Sonnet variant scoring 2.4 points higher than STORM overall; it also beats popular LLMs and reasoning models overall, though reasoning models score better on rubrics. The system’s attribution quality, measured with ALCE-style citation precision/recall, is far ahead of other QA systems. In 9 weeks of public deployment, it received 30.2k questions from 8,219 unique visitors; responses averaged about 2.4k words and cost $0.50 to produce, and 85% of 2,433 thumbs feedback was positive. Average full report generation took 2.5 minutes (N=500, σ=70s), with the first section available in 50 seconds (N=500, σ=24s).

Methods

The pipeline has three main components: retrieval from Semantic Scholar keyword search over abstracts and a new `/snippet/search` endpoint over 11.7M full-text papers and 285.6M passages; cross-encoder re-ranking retaining the top 50 passages; and multi-step LLM generation that extracts quotes, builds a thematic outline, clusters quotes, and synthesizes sections with citations and comparison tables. Retrieval tuning used 500 synthetic queries and top 1000 passages per query, with GPT-4o relevance labels that agreed with human annotators 80% of the time on a sample of 100 queries; re-ranker evaluation used 2,426 queries and 225,618 passages. Generation was evaluated on ScholarQA-CS, consisting of expert-annotated rubrics for 100 Computer Science research questions, using GPT-4o as judge and comparing against LLM prompting and RAG QA baselines.

Limitations

The paper states that production uses a proprietary, closed-source LLM backbone, and that open-source models lag behind proprietary models in their evaluation; they are experimenting with open-source replacements. The paper also states that evaluation is limited to Computer Science questions and relies completely on an LLM evaluator, with ongoing work on broader-domain, human-preference benchmarks. User feedback analysis of 383 neutral/negative freeform instances identified categories including incorrect or missing references (126), off-topic or misunderstood query (113), requests for more detail or specificity (289), general quality feedback (149), and language or format issues (78).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.18653/v1/2025.acl-demo.49

Cites

5 of 6 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Image 1
CC-BY · From the PDF
Figure 2 Multi-section [B] report generated by Scholar QA. References are linked to supporting excerpts [C]. Thumbs and free text feedback are collected for the full report [A], and also for each section and inline table.
Figure 2 Multi-section [B] report generated by Scholar QA. References are linked to supporting excerpts [C]. Thumbs and free text feedback are collected for the full report [A], and also for each section and inline table.CC-BY · From the PDF
Figure 3 Inline tables compare papers [A] with common aspects [B] with values linked to supporting excerpts from the papers [C].
Figure 3 Inline tables compare papers [A] with common aspects [B] with values linked to supporting excerpts from the papers [C].CC-BY · From the PDF
Figure 4 Embedding ranking performance for various compression methods and matryoshka cutoffs. The xaxis indicates the size of the vector index based relative to using int8 quantization and the full embedding size. The red circle indicates the selected configuration. Embedding size is notated next to each point.
Figure 4 Embedding ranking performance for various compression methods and matryoshka cutoffs. The xaxis indicates the size of the vector index based relative to using int8 quantization and the full embedding size. The red circle indicates the selected configuration. Embedding size is notated next to each point.CC-BY · From the PDF
Table 4: Embedding Models to optimize retrieval
Table 4: Embedding Models to optimize retrievalCC-BY · From the PDF
Figure 6 Ranking performance for various ensembles with relative size of the index required. Excluding SparseEmbed reduces the index size by 20% without a significant drop in ranking performance.
Figure 6 Ranking performance for various ensembles with relative size of the index required. Excluding SparseEmbed reduces the index size by 20% without a significant drop in ranking performance.CC-BY · From the PDF
Figure 7 Progress indication and section streaming.
Figure 7 Progress indication and section streaming.CC-BY · From the PDF
Figure 8 Distribution of different question types submitted to Scholar QA deployed Web application.
Figure 8 Distribution of different question types submitted to Scholar QA deployed Web application.CC-BY · From the PDF

Verification

Claims the paper makes

21 Own result · 18 Sources not found · 13 Own method · 10 Own interpretation · 4 No basis given · 1 Check pending · 1 Not checked yet

Verify all 2 references — free account
  • It may state two separate claims as one. 19
  • Probably not a substantial claim. 29
39 of 39
  • The paper relies on this without support.

    Most state-of-the-art scientific QA systems are expensive to use and closed source, relying on models, workflows, and retrieval solutions not shared publicly.

    “Most of these systems are expensive to use and closed source, relying on models, workflows, and retrieval solutions not shared publicly.”
  • The paper relies on this without support.

    Effective organization of reference materials is essential for the overall coherence of a comprehensive research report.

    “For generating a comprehensive research report, the effective organization of reference materials is essential for its overall coherence.”
  • The paper relies on this without support.

    Paragraphs convey nuanced summaries from multiple papers, while bulleted lists enumerate related papers.

    “Paragraphs convey nuanced summaries from multiple papers, while bulleted lists enumerate related papers (e.g., models, datasets, or interactive systems).”
  • The paper relies on this without support.

    The ScholarQA-CS benchmark rewards attribution and supporting evidence as a measure of trust in the system.

    “The benchmark rewards attribution and supporting evidence as a measure of trust in the system”
  • The paper relies on this without support.

    Commercial platforms can be too expensive for widespread academic use and typically lack transparency regarding their pipelines.

    “Although these platforms leverage advanced retrieval and generation capabilities to facilitate literature reviews and deliver rapid insights, they can be too expensive for widespread academic use and typically lack transparency regarding their pipelines.”
5 of 5

Citations

Cites

Show all 5 references

Research works printed but not linked: 16

  1. Shubham Agarwal, Gaurav Sahu, Abhay Puri, Is- sam Hadj Laradji, Krishnamurthy Dj Dvijotham, Ja- son Stanley, Laurent Charlin, and Christopher Pal. 2024. Litllms, llms for literature review: Are we there yet? Anthropic. 2024. The claude 3 model family: Opus, sonnet, haiku. Anthropic. 2025. Claude 3.7 sonnet system card.
  2. Akari Asai, Jacqueline He, Rulin Shao, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D'Arcy, David Wadden, Matt Latzke, Minyang Tian, Pan Ji, Shengyan Liu, Hao Tong, Bohao Wu, Yanyu Xiong, Luke S. Zettlemoyer, and 6 others. 2024. Openscholar: Synthesizing scientific literature with retrieval-augmented lms. ArXiv, abs/2411.14199. · arXiv
  3. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-rag: Learning to retrieve, generate, and critique through self-reflection. ArXiv, abs/2310.11511. · arXiv
  4. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony S. Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Ko- renev, Arthur Hinsvark, Arun Rao, Aston Zhang, and 510 others. 2024. The llama 3 herd of models. ArXiv, abs/2407.21783. · arXiv
  5. Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023. Enabling large language models to generate text with citations. In Conference on Empirical Meth- ods in Natural Language Processing.
  6. OpenAI Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Rad- ford, Aleksander Mkadry, Alex Baker-Whitcomb, Alex Beutel, Alex Borzunov, Alex Carney, Alex Chow, Alexander Kirillov, Alex Nichol, Alex Paino, and 397 others. 2024. Gpt-4o system card. ArXiv, abs/2410.21276. · arXiv
  7. Rodney Michael Kinney, Chloe Anastasiades, Rus- sell Authur, Iz Beltagy, Jonathan Bragg, Alexan- dra Buraczynski, Isabel Cachola, Stefan Candra, Yo- ganand Chandrasekhar, Arman Cohan, Miles Craw- ford, Doug Downey, Jason Dunkelberger, Oren Et- zioni, Rob Evans, Sergey Feldman, Joseph Gorney, David W. Graham, F.Q. Hu, and 29 others. 2023. The semantic scholar open data platform. ArXiv, abs/2301.10140. · arXiv
  8. Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Hein- rich Kuttler, Mike Lewis, Wen tau Yih, Tim Rock- täschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-augmented generation for knowledge- intensive nlp tasks. ArXiv, abs/2005.11401. · arXiv
  9. Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers. 2022. Mteb: Massive text embedding benchmark. In Conference of the European Chapter of the Association for Computational Linguistics.
  10. Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel S. Weld, Joseph Chee Chang, and Kyle Lo. 2024. Arx- ivdigestables: Synthesizing scientific literature into tables using language models. In Conference on Em- pirical Methods in Natural Language Processing. 520
  11. Jakob Nielsen. 1994. Enhancing the explanatory power of usability heuristics. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems, pages 152-158.
  12. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Associa- tion for Computational Linguistics.
  13. Yijia Shao, Yucheng Jiang, Theodore A. Kanell, Pe- ter Xu, Omar Khattab, and Monica S. Lam. 2024b. Assisting in writing wikipedia-like articles from scratch with large language models. Preprint, arXiv:2402.14207. · arXiv
  14. Michael D. Skarlinski, Sam Cox, Jon M. Laurent, James D. Braza, Michaela M. Hinks, Michael J Hammerling, Manvitha Ponnapati, Samuel G. Ro- driques, and Andrew D. White. 2024. Language agents achieve superhuman synthesis of scientific knowledge. ArXiv, abs/2409.13740. · arXiv
  15. Aviv Slobodkin, Eran Hirsch, Arie Cattan, Tal Schuster, and Ido Dagan. 2024. Attribute first, then generate: Locally-attributable grounded text generation. In Annual Meeting of the Association for Computational Linguistics.
  16. Aivin V. Solatorio. 2024. Gistembed: Guided in-sample selection of training negatives for text embedding fine-tuning. ArXiv, abs/2402.16829. · arXiv

Cited by

Sorted by citations

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Metadata

Authors
Singh, A., Chang, J. C., Haddad, D., Naik, A., Hwang, J. D., Kinney, R., Weld, D. S., Downey, D., & Feldman, S.
Affiliations
Allen Institute for Artificial Intelligence (US)
Venue
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Published
2025
Pages
513-523
ISSN
0736-587X (ISSN-L)
OpenAlex
W4412886885
Cited by
19
References
5
Citations by year
2026: 13 · 2025: 6
FWCI
6.99
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Keywords
question answering, literature synthesis, attribution, AI-assisted research, scientific information retrieval

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-11-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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