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.
@article{arxiv.2406.07257,
title = {Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway},
author = {Hamed Babaei Giglou and Tilahun Abedissa Taffa and Rana Abdullah and Aida Usmanova and Ricardo Usbeck and Jennifer D'Souza and Sören Auer},
journal= {arXiv preprint arXiv:2406.07257},
year = {2024}
}
Comments
13 pages main content, 16 pages overall, 3 Figures, accepted for publication at NSLP 2024 workshop at ESWC 2024