Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway

Hamed Babaei Giglou; Tilahun Abedissa Taffa; Rana Abdullah; Aida Usmanova; Ricardo Usbeck; Jennifer D’Souza; Sören Auer

doi:10.48550/arXiv.2406.07257

Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway

Hamed Babaei Giglou
, Tilahun Abedissa Taffa^*
, Rana Abdullah
, Aida Usmanova
, Ricardo Usbeck
, Jennifer D’Souza
, Sören Auer

^*Korrespondierende/r Autor/-in für diese Arbeit

Professur für Wirtschaftsinformatik, insbesondere Künstliche Intelligenz und Erklärbarkeit

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

2 Zitate (Scopus)

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.

Originalsprache	Englisch
Titel	Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings
Redakteure/-innen	Georg Rehm, Stefan Dietze, Sonja Schimmler, Frank Krüger
Seitenumfang	16
Herausgeber (Verlag)	Springer Science and Business Media Deutschland
Erscheinungsdatum	2024
Seiten	3-18
ISBN (Print)	978-3-031-65793-1
ISBN (elektronisch)	978-3-031-65794-8
DOIs	https://doi.org/10.48550/arXiv.2406.07257 https://doi.org/10.1007/978-3-031-65794-8_1
Publikationsstatus	Erschienen - 2024
Veranstaltung	1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024 - Hersonissos, Hersonissos, Griechenland Dauer: 27.05.2024 → 27.05.2024 Konferenznummer: 1 https://nfdi4ds.github.io/nslp2024/

Bibliographische Notiz

Publisher Copyright:
© The Author(s) 2024.

Fachgebiete und Schlagwörter

Informatik
Wirtschaftsinformatik

Zugriff auf Dokument

10.48550/arXiv.2406.07257Lizenz: Urheberrechtlich geschützt
10.1007/978-3-031-65794-8_1Lizenz: CC BY

NFDI4DS - NFDI für Datenwissenschaften und Künstliche Intelligenz
Usbeck, R. (Wissenschaftliche Projektleiter*in)
Deutsche Forschungsgemeinschaft
01.01.24 → 30.09.26
Projekt: Forschung

Dieses zitieren

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 (Hrsg.), Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings (S. 3-18). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14770 LNAI). Springer Science and Business Media Deutschland. https://doi.org/10.48550/arXiv.2406.07257, https://doi.org/10.1007/978-3-031-65794-8_1

Babaei Giglou, Hamed ; Taffa, Tilahun Abedissa ; Abdullah, Rana et al. / Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway. Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings. Hrsg. / Georg Rehm ; Stefan Dietze ; Sonja Schimmler ; Frank Krüger. Springer Science and Business Media Deutschland, 2024. S. 3-18 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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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",

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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 (Hrsg.), 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), Bd. 14770 LNAI, Springer Science and Business Media Deutschland, S. 3-18, 1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024, Hersonissos, Griechenland, 27.05.24. https://doi.org/10.48550/arXiv.2406.07257, https://doi.org/10.1007/978-3-031-65794-8_1

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. Hrsg. / Georg Rehm; Stefan Dietze; Sonja Schimmler; Frank Krüger. Springer Science and Business Media Deutschland, 2024. S. 3-18 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14770 LNAI).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

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 - https://www.scopus.com/pages/publications/85202151417

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

T2 - 1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024

Y2 - 27 May 2024 through 27 May 2024

ER -

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, Hrsg., Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings. Springer Science and Business Media Deutschland. 2024. S. 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

Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway

Abstract

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NFDI4DS - NFDI für Datenwissenschaften und Künstliche Intelligenz

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