Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910482041929728 |
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| author | Giglou, Hamed Babaei Taffa, Tilahun Abedissa Abdullah, Rana Usmanova, Aida Usbeck, Ricardo D'Souza, Jennifer Auer, Sören |
| author_facet | Giglou, Hamed Babaei Taffa, Tilahun Abedissa Abdullah, Rana Usmanova, Aida Usbeck, Ricardo D'Souza, Jennifer Auer, Sören |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway Giglou, Hamed Babaei Taffa, Tilahun Abedissa Abdullah, Rana Usmanova, Aida Usbeck, Ricardo D'Souza, Jennifer Auer, Sören Computation and Language Artificial Intelligence 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. |
| title | Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.07257 |