Natural Language Processing in the Nuclear Industry: Opportunities and Challenges
Bibliographic record
Abstract
Natural language processing (NLP) has significant potential within the nuclear industry, yet no prior surveys have focused exclusively on its applications in this sector. Addressing this gap, this review explores recent studies leveraging NLP to enhance key areas, such as equipment reliability, maintenance, compliance, safety, verification, control systems, human-system interfaces, knowledge extraction, and decision-making support in nuclear power plants (NPPs). Our analysis reveals that NLP techniques have successfully automated maintenance recommendations, extracted structured insights from work orders, improved compliance verification, and optimized human-system interactions in NPPs. These advancements have contributed to operational efficiency, cost reduction, and enhanced safety.This paper also examines the unique challenges of implementing NLP in nuclear settings, including regulatory constraints, data quality issues, domain-specific language complexities, and the integration of large language models (LLMs). To address these challenges, studies have proposed techniques, such as domain-specific dictionaries for handling nuclear terminology, hybrid models combining rule-based and machine learning approaches, and retrieval-augmented generation to improve interpretability and accuracy.Future directions are proposed, highlighting the importance of real-world testing, model refinement, and the broader adoption of LLMs to improve operational efficiency and safety in NPPs. As the nuclear industry moves toward increased automation, NLP will play a crucial role in bridging the gap between unstructured textual data and actionable intelligence, driving further innovations in safety and decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".