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

2025-06-10

AI Summary

The nuclear industry possesses vast quantities of highly sensitive, low-resource unstructured text, which severely constrains the training and evaluation of large language models (LLMs) due to data scarcity and stringent privacy requirements. Method: We propose the first synthetic question-answer (QA) pair generation framework tailored to the nuclear domain, jointly optimizing information fidelity and regulatory compliance. Our approach integrates LLM-driven information extraction, domain-adaptive question generation, multi-dimensional self-assessment of QA quality, and a nuclear-domain rule-guided post-processing module. Contribution/Results: Evaluated on real nuclear documentation, the framework generates over 10,000 high signal-to-noise-ratio QA pairs. Downstream QA and knowledge retrieval tasks achieve significant accuracy improvements; the generated data has already been deployed to support multiple operational decision-assistance prototype systems.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Bridging unstructured nuclear text data to structured LLM inputs
• Addressing data scarcity and privacy in nuclear LLM applications
• Enabling nuclear knowledge sharing via synthetic Q&A generation

Innovation

• Synthetic data generation for LLM training
• Transforming text into Q&A pairs automatically
• Leveraging LLMs to evaluate synthetic data