Visual large language models for welding assessment

This paper evaluates the effectiveness of visual large language models (LLMs) for weld defect identification, focusing on their potential utility for novice welders. Using the Gemma-3B, Gemma-27B and Qwen2.5-VL-32B models, we benchmark performance against a standardized weld defect dataset and compare against a most modern version of the more traditional YOLO architecture, YOLOv12. Results show the 27B model achieves 66.36 % recall and a lower precision of 46.10 %, while the 3B model demonstrates poor reliability at 35.05 % recall, comparable to the results of the YOLOv12. Meanwhile, Qwen2.5-VL-32B does not produce sufficiently reliable results to gauge them automatically. We conclude that large LLMs can achieve quantitatively superior results on difficult datasets by leveraging innate understanding of welding stemming from their massive pre-training data, allowing improved functionality compared to current state of the art object detectors, and would appear to be beneficial when used in aid of novice welders in training.