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

arXiv: https://arxiv.org/abs/2506.08750 · HTML: https://mlsanity.com/paper/2506.08750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 5 of 5)

- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows](https://mlsanity.com/paper/2402.10379/) (2024)
- [RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework](https://mlsanity.com/paper/2408.01262/) (2024)
- [Synthetic Data Generation with LLM for Improved Depression Prediction](https://mlsanity.com/paper/2411.17672/) (2024)
- [Synthetic Data Generation with Large Language Models for Personalized Community Question Answering](https://mlsanity.com/paper/2410.22182/) (2024)

## Abstract (arXiv)

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.