用开源模型搭建科学文献问答系统，保护科研数据隐私
Retrieval-Augmented Question Answering over Scientific Literature for the Electron-Ion Collider
- 基于arXiv的EIC文献构建本地知识库，结合LLaMA生成答案
- 无需上传未发表数据，实现安全高效的领域问答
- 适合实验核物理领域研究人员快速获取专业信息
为提升语言模型在回答特定领域技术问题中的能力，本工作开发了一款受检索增强生成（RAG）启发的问答应用。该系统基于自建数据库，索引了与电子-离子对撞机（EIC）实验相关的arXiv论文，并采用开源LLaMA模型生成答案。这是此前基于专有模型和云端外部知识库系统的延伸，现改为本地部署的RAG系统，提供成本低、资源受限环境下的可行替代方案。该架构保障数据隐私，避免将预发布科学数据发送至公共领域。未来将扩展知识库至异构的EIC相关出版物与报告，并将应用流程升级至LangGraph框架。
原文摘要 · Abstract (English)
To harness the power of Language Models in answering domain specific specialized technical questions, Retrieval Augmented Generation (RAG) is been used widely. In this work, we have developed a Q\&A application inspired by the Retrieval Augmented Generation (RAG), which is comprised of an in-house database indexed on the arXiv articles related to the Electron-Ion Collider (EIC) experiment - one of the largest international scientific collaboration and incorporated an open-source LLaMA model for answer generation. This is an extension to it's proceeding application built on proprietary model and Cloud-hosted external knowledge-base for the EIC experiment. This locally-deployed RAG-system offers a cost-effective, resource-constraint alternative solution to build a RAG-assisted Q\&A application on answering domain-specific queries in the field of experimental nuclear physics. This set-up facilitates data-privacy, avoids sending any pre-publication scientific data and information to public domain. Future improvement will expand the knowledge base to encompass heterogeneous EIC-related publications and reports and upgrade the application pipeline orchestration to the LangGraph framework.
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