Retrieval-Augmented Question Answering over Scientific Literature for the Electron-Ion Collider
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 o...
| Authors | Tina. J. Jat, T. Ghosh, Karthik Suresh |
|---|---|
| Primary category | hep-ex |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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