Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data
This addresses data scarcity and privacy issues for the nuclear industry, though it is incremental as it applies existing synthetic data methods to a new domain.
The paper tackles the problem of unstructured text data in the nuclear industry by using synthetic data generation to create clean question-answer pairs, enabling the development of robust large language models for tasks like information retrieval and decision-making.
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.