Interpretable Verification Mechanism for Trustworthy Industrial Large Model in Intelligent Manufacturing

The hallucination and black-box nature of Large Models limit their industrial applications. To address these challenges, a verification mechanism built on confidence intervals of Transformer-based output layers is proposed for trustworthy Industrial Large Models (ILMs). Adopting a Vision Transformer (ViT), customized verification operations are incorporated to monitor the forward propagation process, and samples with probability distributions outside confidence intervals exit the network early and are handed over to technicians. Thus, the ViT is more interpretable because only samples within confidence intervals can propagate forward and be output from the ViT. Subsequently, an over-approximation approach is employed to obtain confidence intervals by linearizing the decision boundary of the ViT. The conservative decision boundary serves as the lower bound of confidence intervals, which can provide provable robustness for confidence intervals because the minimum probability of the ground truth is always higher than that of other samples. Finally, a certified training strategy is employed to enhance the robustness of the ViT. Data disturbances with Gaussian noise are generated using a randomized smoothing strategy to augment the data distribution. A smoothed loss function is used to strengthen the robustness of the ViT against data disturbances, thereby enabling greater confidence intervals. The proposed verification mechanism was validated on two public defect datasets. It achieved 99.98% precision for normal samples and approximately 95% precision for defective samples on a fabric defect dataset. It also achieved 99.21% precision and 99.15% F1 score on a wafer defect dataset. Comparative experiments with other Transformer-based models also demonstrated the generalization ability of the proposed verification mechanism.