Large Language Model Integration for Knowledge Retrieval and Interaction for the DUNE Experiment

TL;DR

This paper introduces DUNE-GPT, a framework that uses large language models and retrieval-augmented generation to facilitate natural-language access to DUNE's complex technical documentation and data, enhancing collaboration and knowledge management.

Contribution

The work presents a novel LLM-based system tailored for the DUNE experiment, integrating retrieval-augmented generation to improve knowledge retrieval within a high-energy physics context.

Findings

Enables natural-language querying of DUNE documentation

Maintains data privacy and infrastructure compliance

Improves collaboration efficiency

Abstract

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that will generate an unprecedented volume of heterogeneous information-from documentation and technical notes to experimental data and reconstruction pipelines. Efficient knowledge retrieval and contextual understanding are increasingly critical for collaboration-wide productivity and onboarding. In this work, we present DUNE-GPT, a prototype framework that leverages large language models (LLMs) and retrieval-augmented generation (RAG) to enable natural-language querying of DUNE's internal documentation and technical resources. The system provides an intelligent interface for DUNE collaborators to interact with experiment-specific knowledge while maintaining data privacy and infrastructure compliance within Fermilab computing resources.