Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
<|reference_start|>Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway: 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.<|reference_end|>
@article{giglou2024scholarly,
    title={Scholarly Question Answering using Large Language Models in the
  NFDI4DataScience Gateway},
    author={Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah, Aida
  Usmanova, Ricardo Usbeck, Jennifer D'Souza, S"oren Auer},
    journal={arXiv preprint arXiv:2406.07257},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07257},
    primaryClass={cs.CL cs.AI}
}