Model Metrology

Model metrology focuses on developing robust methods for evaluating and characterizing the performance of models, particularly in complex systems like language models and those used in industrial metrology. Current research emphasizes data-driven approaches, including machine learning algorithms like deep learning (e.g., fine-tuned Segment Anything Models) and Gaussian Processes, to improve accuracy and efficiency in both model assessment and uncertainty quantification. This field is crucial for ensuring the trustworthiness and reliability of AI systems across diverse applications, from manufacturing process optimization to reliable scientific measurements, by providing rigorous benchmarks and quantifiable metrics.