Summary of Scholarly Question Answering Using Large Language Models in the Nfdi4datascience Gateway, by Hamed Babaei Giglou et al.
Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
by Hamed Babaei Giglou, Tilahun Abedissa Taffa, Rana Abdullah, Aida Usmanova, Ricardo Usbeck, Jennifer D’Souza, Sören Auer
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a Question Answering (QA) system built on top of the NFDI4DataScience Gateway, utilizing a Retrieval Augmented Generation-based (RAG) approach. This system employs a Large Language Model (LLM) to facilitate dynamic interaction with search results and enhance filtering capabilities. The effectiveness of both the Gateway and the QA system is demonstrated through experimental analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way for people to ask questions and get answers from lots of different scientific databases. It uses a special type of computer model that can understand natural language, which makes it easier to search and find what you’re looking for. This is important because scientists need to be able to easily find and use information from many different places. |
Keywords
» Artificial intelligence » Large language model » Question answering » Rag » Retrieval augmented generation
