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.

本研究介绍了一个基于 NFDI4DataScience Gateway 的学术问答系统，采用了检索增强生成模型（RAG）的方法。该系统利用强大的大语言模型提供动态交互和会话式搜索，通过实验分析证明了 Gateway 和学术问答系统的有效性。