Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data

TL;DR

This paper demonstrates how synthetic data generation enables the use of large language models in the nuclear industry by transforming unstructured text into structured question-answer pairs, addressing data scarcity and privacy issues.

Contribution

It introduces a method for generating synthetic question-answer data from unstructured nuclear industry texts to facilitate LLM applications, overcoming data scarcity and privacy challenges.

Findings

Synthetic data improves LLM training in nuclear domain

Enhanced information retrieval from unstructured texts

Supports privacy-preserving data sharing

Abstract

The nuclear industry possesses a wealth of valuable information locked away in unstructured text data. This data, however, is not readily usable for advanced Large Language Model (LLM) applications that require clean, structured question-answer pairs for tasks like model training, fine-tuning, and evaluation. This paper explores how synthetic data generation can bridge this gap, enabling the development of robust LLMs for the nuclear domain. We discuss the challenges of data scarcity and privacy concerns inherent in the nuclear industry and how synthetic data provides a solution by transforming existing text data into usable Q&A pairs. This approach leverages LLMs to analyze text, extract key information, generate relevant questions, and evaluate the quality of the resulting synthetic dataset. By unlocking the potential of LLMs in the nuclear industry, synthetic data can pave the way for improved information retrieval, enhanced knowledge sharing, and more informed decision-making in this critical sector.