ESAC (EQ-SANS Assisting Chatbot): Application of Large Language Models and Retrieval-Augmented Generation for Enhanced User Experience at EQ-SANS

TL;DR

This paper presents ESAC, a chatbot that uses large language models and retrieval-augmented generation to improve user support and interaction for neutron scattering experiments at EQ-SANS, making complex instrumentation more accessible.

Contribution

The paper introduces ESAC, a novel LLM-based chatbot with RAG technology, specifically designed to assist users in operating the EQ-SANS instrument more effectively.

Findings

Enhanced user experience at EQ-SANS with the chatbot.

Bridging user knowledge gaps through interactive support.

Set a new standard for user interaction in scientific facilities.

Abstract

Neutron scattering experiments have played vital roles in exploring materials properties in the past decades. While user interfaces have been improved over time, neutron scattering experiments still require specific knowledge or training by an expert due to the complexity of such advanced instrumentation and the limited number of experiments each person may perform each year. This paper introduces an innovative chatbot application that leverages Large Language Models(LLM) and Retrieval-Augmented Generation (RAG) technologies to significantly enhance the user experience at the EQ-SANS, a small-angle neutron scattering instrument at the Spallation Neutron Source of Oak Ridge National Laboratory. Through a user-centric design approach, the EQ-SANS Assisting Chatbot (ESAC) serves as an interactive reference for users, thereby facilitating the use of the instrument by visiting scientists. By bridging the gap between the users of EQ-SANS and the control systems required to perform their experiments, the ESAC sets a new standard for interactive learning and support for the scientific community using large-scale scientific facilities.