Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah, Aida Usmanova, Ricardo Usbeck +2 more
6/11/2024
cs.CLcs.AI

No official code found. Best implementation: semantic-systems/nfdi-search-engine-chatbot (5 stars, MIT, last commit 2025-12-12).

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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Code Implementations(1)

semantic-systems/nfdi-search-engine-chatbotCommunity100%
MIT

NFDI4DS Search Engine ChatBot

50Python, DockerfileJun 29, 202310 months agoMIT
chatbotlangchainllmsnfdi4dssoftware-engineering

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Cite this paper

@article{giglou2024scholarly,
  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},
  year   = {2024},
  eprint = {2406.07257},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.07257v1}
}

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