# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
## 6 Discussion
### Generalization of WorkloadGPT across Pilots (Q2)  
During the development of WorkloadGPT, pilot IDs were added as variables in the \"instruction\" part of the formatted instances, along with relevant prompts indicating \"pilots have individual differences.\" After training and fine-tuning, WorkloadGPT achieved an accuracy of 87.3% on a validation set, with a cross-pilot standard deviation of accuracy of about 2.1%, demonstrating its strong generalization capability. In previous studies, generalization was typically achieved through specific algorithm designs:  
**CNN-DG [74]:** The CNN-DG model for cross-pilot workload recognition (CWR) leverages convolutional neural networks (CNNs) and domain generalization (DG) to overcome CWR challenges. It consists of three primary components: a deep feature extractor, a label classifier, and a domain generalizer. The model enhances domain generalizability through adversarial training. **Kmeans + SVM [75]:** The K-means + SVM model integrates k-means clustering with support vector machines (SVMs) to mitigate individual differences in workload assessments. It comprises two main components: a clustering module and a classification module. Initially, the clustering module groups participants based on physiological data features. Subsequently, the classification module uses SVM within each cluster to classify and predict data. Using the same training and validation sets as WorkloadGPT, CNN-DG and Kmeans + SVM were trained and tested. The results are shown in Figure 10. For CNN-DG, the cross-pilot standard deviation was 1.9%, but the accuracy was only 63.7%. This could have been due to the adversarial training process overlooking key factors that influence workload classification. Consequently, the model failed to adequately learn the essential features required to distinguish between different workload states, resulting in low model accuracy. On the other hand, Kmeans + SVM resulted in a cross-pilot standard deviation of 8.2% and a mean accuracy of 79.1%. The cross-pilot standard deviation was nearly four times higher than that of WorkloadGPT, and the model's accuracy was also lower than WorkloadGPT. The suboptimal performance of the Kmeans + SVM model may be attributed to several factors. Firstly, the k-means algorithm requires pre-specifying the number of clusters, which is difficult to determine accurately in practical applications, leading to poor clustering performance. Secondly, k-means is sensitive to the initial cluster centers, and different initializations can affect the model's stability and predictive performance. Additionally, the complexity and diversity of individual differences make it challenging for simple clustering and classification methods to capture all relevant information comprehensively. Furthermore, imbalanced data distribution and noise can also reduce the model's performance. Figure 9: Comparison of results from different models. In contrast, WorkloadGPT employs an end-to-end approach, achieving robust generalization efficiently and with high quality, effectively addressing the limitations of the aforementioned models. Firstly, WorkloadGPT ensures that the large language model focuses on individual differences by including prompts in the training dataset, capturing key features that are crucial for classification predictions and have generalizability. This approach overcomes the limitations of previous methods that failed to comprehensively capture useful information, effectively capturing the characteristics of different pilots. Secondly, WorkloadGPT avoids the inherent issues of the k-means clustering algorithm, which requires pre-specifying the number of clusters--a challenging task to determine accurately in practical applications, potentially leading to suboptimal clustering results. Additionally, WorkloadGPT does not rely on initial cluster centers, thus avoiding inconsistencies in clustering outcomes due to different initializations, thereby enhancing the model's stability and predictive performance.