October 12, 2025Open Access

A Comparative Benchmark of Large Language Models for Labelling Wind Turbine Maintenance Logs

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A clear performance hierarchy among large language models was identified, with top performers showing strong alignment with benchmark standards.
The evaluation demonstrated that classification accuracy depends significantly on the task's semantic ambiguity, affecting model performance results.
Open-source tools were developed to promote transparency and facilitate further research into wind turbine maintenance log classification efforts.
The study advocates for integrating a Human-in-the-Loop system, enhancing data quality through collaboration between LLMs and human experts.

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This framework evaluates large language models for classifying wind turbine maintenance logs, suggesting improvements in operational efficiency.

Malyi et al. (2025) studied this question.

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This framework evaluates large language models for classifying wind turbine maintenance logs, suggesting improvements in operational efficiency.