Standard
Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway. / Babaei Giglou, Hamed
; Taffa, Tilahun Abedissa; Abdullah, Rana et al.
Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings. ed. / Georg Rehm; Stefan Dietze; Sonja Schimmler; Frank Krüger. Springer Science and Business Media Deutschland GmbH, 2024. p. 3-18 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14770 LNAI).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Babaei Giglou, H
, Taffa, TA, Abdullah, R, Usmanova, A, Usbeck, R, D’Souza, J & Auer, S 2024,
Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway. in G Rehm, S Dietze, S Schimmler & F Krüger (eds),
Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14770 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 3-18, 1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024, Hersonissos, Greece,
27.05.24.
https://doi.org/10.48550/arXiv.2406.07257,
https://doi.org/10.1007/978-3-031-65794-8_1APA
Babaei Giglou, H.
, Taffa, T. A., Abdullah, R., Usmanova, A., Usbeck, R., D’Souza, J., & Auer, S. (2024).
Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway. In G. Rehm, S. Dietze, S. Schimmler, & F. Krüger (Eds.),
Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings (pp. 3-18). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14770 LNAI). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.48550/arXiv.2406.07257,
https://doi.org/10.1007/978-3-031-65794-8_1Vancouver
Babaei Giglou H
, Taffa TA, Abdullah R, Usmanova A, Usbeck R, D’Souza J et al.
Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway. In Rehm G, Dietze S, Schimmler S, Krüger F, editors, Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 3-18. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2024 Aug 15. doi: 10.48550/arXiv.2406.07257, 10.1007/978-3-031-65794-8_1
Bibtex
@inbook{1d41237f11ca4e6ca7976e08a4c0bf35,
title = "Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway",
abstract = "This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and the scholarly QA system is demonstrated through experimental analysis.",
keywords = "Federated Search, Large Language Models, NFDI4DS Gateway, Retrieval Augmented Generation, Scholarly Question Answering, Informatics, Business informatics",
author = "{Babaei Giglou}, Hamed and Taffa, {Tilahun Abedissa} and Rana Abdullah and Aida Usmanova and Ricardo Usbeck and Jennifer D{\textquoteright}Souza and S{\"o}ren Auer",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.; 1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024, NSLP 2024 ; Conference date: 27-05-2024 Through 27-05-2024",
year = "2024",
doi = "10.48550/arXiv.2406.07257",
language = "English",
isbn = "978-3-031-65793-1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--18",
editor = "Georg Rehm and Stefan Dietze and Sonja Schimmler and Frank Kr{\"u}ger",
booktitle = "Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings",
address = "Germany",
url = "https://nfdi4ds.github.io/nslp2024/",
}
RIS
TY - CHAP
T1 - Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway
AU - Babaei Giglou, Hamed
AU - Taffa, Tilahun Abedissa
AU - Abdullah, Rana
AU - Usmanova, Aida
AU - Usbeck, Ricardo
AU - D’Souza, Jennifer
AU - Auer, Sören
N1 - Conference code: 1
PY - 2024
Y1 - 2024
N2 - This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and the scholarly QA system is demonstrated through experimental analysis.
AB - This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and the scholarly QA system is demonstrated through experimental analysis.
KW - Federated Search
KW - Large Language Models
KW - NFDI4DS Gateway
KW - Retrieval Augmented Generation
KW - Scholarly Question Answering
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85202151417&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2406.07257
DO - 10.48550/arXiv.2406.07257
M3 - Article in conference proceedings
SN - 978-3-031-65793-1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 18
BT - Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings
A2 - Rehm, Georg
A2 - Dietze, Stefan
A2 - Schimmler, Sonja
A2 - Krüger, Frank
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024
Y2 - 27 May 2024 through 27 May 2024
ER -
