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.

翻译：本文介绍了一种基于NFDI4DataScience网关的学术问答（QA）系统，该系统采用基于检索增强生成（RAG）的方法。NFDI4DS网关作为基础框架，提供统一直观的接口，支持通过联邦检索查询各类科学数据库。基于RAG的学术问答系统以大语言模型（LLM）为驱动，能够实现与检索结果的动态交互，增强过滤能力，并促进与网关搜索的对话式交互。通过实验分析，验证了该网关及学术问答系统的有效性。