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Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

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arxiv 2406.07257 v1 pith:XDNYQIEK submitted 2024-06-11 cs.CL cs.AI

Hamed Babaei Giglou , Tilahun Abedissa Taffa , Rana Abdullah , Aida Usmanova , Ricardo Usbeck , Jennifer D'Souza , Sören Auer This is my paper · ORCID
classification cs.CLcs.AI
keywords gatewayscholarlysearchansweringlanguagelargenfdi4datasciencequestion
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs

    cs.IR 2025-01 conditional novelty 6.0 of 10

    A citation-graph retrieval framework that entangles sparse and dense relevance signals in a GNN over paper chunks reports state-of-the-art Hit@1 and answer accuracy on two research QA benchmarks.

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