TY - GEN

T1 - Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

AU - Giglou, Hamed Babaei

AU - Taffa, Tilahun Abedissa

AU - Abdullah, Rana

AU - Usmanova, Aida

AU - Usbeck, Ricardo

AU - D'Souza, Jennifer

AU - Auer, Sören

N1 - 13 pages main content, 16 pages overall, 3 Figures, accepted for publication at NSLP 2024 workshop at ESWC 2024

PY - 2024/6/11

Y1 - 2024/6/11

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 - cs.CL

KW - cs.AI

M3 - Other

ER -