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

^*Corresponding author for this work

Professorship of Information Systems, in particular Artificial Intelligence and Explainability

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

3 Citations (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.

Original language	English
Title of host publication	Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings
Editors	Georg Rehm, Stefan Dietze, Sonja Schimmler, Frank Krüger
Number of pages	16
Publisher	Springer Science and Business Media Deutschland
Publication date	2024
Pages	3-18
ISBN (Print)	978-3-031-65793-1
ISBN (Electronic)	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
Publication status	Published - 2024
Event	1st International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs - NSLP 2024 - Hersonissos, Hersonissos, Greece Duration: 27.05.2024 → 27.05.2024 Conference number: 1 https://nfdi4ds.github.io/nslp2024/

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Research areas and keywords

Federated Search
Large Language Models
NFDI4DS Gateway
Retrieval Augmented Generation
Scholarly Question Answering
Informatics
Business informatics

Access to Document

10.48550/arXiv.2406.07257Licence: In Copyright
10.1007/978-3-031-65794-8_1Licence: CC BY

NFDI4DS - NFDI for Data Science and Artificial Intelligence
Usbeck, R. (Project manager, academic)
German Research Foundation
01.01.24 → 30.09.26
Project: Research

Cite this

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. 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. editor / Georg Rehm ; Stefan Dietze ; Sonja Schimmler ; Frank Krüger. Springer Science and Business Media Deutschland, 2024. pp. 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.",

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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 (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, 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_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. ed. / Georg Rehm; Stefan Dietze; Sonja Schimmler; Frank Krüger. Springer Science and Business Media Deutschland, 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

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AU - Abdullah, Rana

AU - Usmanova, Aida

AU - Usbeck, Ricardo

AU - D’Souza, Jennifer

AU - Auer, Sören

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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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Natural Scientific Language Processing and Research Knowledge Graphs - 1st International Workshop, NSLP 2024, Proceedings

A2 - Rehm, Georg

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A2 - Schimmler, Sonja

A2 - Krüger, Frank

PB - Springer Science and Business Media Deutschland

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

Scholarly Question Answering Using Large Language Models in the NFDI4DataScience Gateway

Abstract

Bibliographical note

Research areas and keywords

Access to Document

Fingerprint

Projects

NFDI4DS - NFDI for Data Science and Artificial Intelligence

Cite this