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。

利用大型语言模型在 NFDI4DataScience Gateway 中进行学术问题回答

Scholarly Question Answering using Large Language Models in the  NFDI4DataScience Gateway

Geoscientists, as well as researchers in many fields, need to read a huge
amount of literature to locate, extract, and aggregate relevant results and
data to enable future research or to build a scientific database, but there is
no existing system to support this use case well. In this paper, based on the
findings of a formative study about how geoscientists collaboratively annotate
literature and extract and aggregate data, we proposed DeepShovel, a
publicly-available AI-assisted data extraction system to support their needs.
DeepShovel leverages the state-of-the-art neural network models to support
researcher(s) easily and accurately annotate papers (in the PDF format) and
extract data from tables, figures, maps, etc. in a human-AI collaboration
manner. A follow-up user evaluation with 14 researchers suggested DeepShovel
improved users' efficiency of data extraction for building scientific
databases, and encouraged teams to form a larger scale but more tightly-coupled
collaboration.

本文介绍了基于多篇研究文献的分析结果，提出了 DeepShovel—— 一个公开的 AI 辅助数据提取系统，可以帮助地球科学家更加便捷、准确地注释论文，提取数据，并在人工智能的协助下构建科学数据库，其用户测试结果表明 DeepShovel 提高了数据提取效率，并鼓励更紧密的合作。