Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
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2024.
Research output: other publications › Other › Research
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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 -