Towards a \textbf{RAG}-based Summarization Agent for the Electron-Ion Collider

arXiv:2403.15729v1 Announce Type: new
Abstract: The complexity and sheer volume of information encompassing documents, papers, data, and other resources from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists. To tackle this issue, a Retrieval Augmented Generation (RAG)--based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial …

abstract agent arxiv career complexity cs.ai cs.cl data demand documents early-career electron forms hep-ex information making papers physics.ins-det rag resources scale scientists summarization type