mlsanityData to 14 Oct 2026

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

arXiv 2506.08750 ↗First posted 10 Jun 2025cs.CLcited by 0

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

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.

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.

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5 of 7 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Synthetic Data Generation with Large Language Models for Personalized Community Question Answering29 Oct
  2. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey14 Jun
  3. 2024DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows16 Feb

Other references · 2

  1. 2024RAGEval: Scenario Specific RAG Evaluation Dataset Generation Frameworkcited by 39
  2. 2024Synthetic Data Generation with LLM for Improved Depression Predictioncited by 13

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  1. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Code
  2. 2024Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs
  3. 2024Evaluating ChatGPT on Nuclear Domain-Specific Data
  4. 2025Enhancing Accuracy and Maintainability in Nuclear Plant Data Retrieval: A Function-Calling LLM Approach Over NL-to-SQL
  5. 2024Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models
  6. 2026Domain-Adaptation through Synthetic Data: Fine-Tuning Large Language Models for German Law
  7. 2024Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
  8. 2025GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation