Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

• DOI: 10.48550/arxiv.2406.07257
• Publication Date: 2024-01-01
• Authors: Hamed Babaei Giglou; Tilahun Abedissa ...; Rana Abdullah; Aida Usmanova; Ricardo Usbeck; Jennifer D’Souza; Sören Auer
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computation and Language (cs.CL)

AbstractThis 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.