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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
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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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Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah, Aida Usmanova, Ricardo Usbeck, Jennifer D'Souza, Sören Auer. 2024-06-11. Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway. https://arxiv.org/abs/2406.07257

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