# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
## 1 Introduction  
Aviation safety has always been a critical issue and an ongoing challenge. As the aviation industry advances, the requirements for ensuring flight safety have become increasingly stringent. Research indicates that under high workload conditions, pilots' reaction times and decision-making accuracy significantly decline, thereby raising the risk of operational errors [1]. To mitigate flight accidents induced by high workloads, optimizing the cockpit display interface layout has become an essential design requirement in the aviation industry. Complex cockpit display interfaces, filled with numerous flight readings and instrument data, must be properly designed to accommodate pilots' workload levels; otherwise, they may adversely affect flight safety [2, 3]. Consequently, measuring pilot workload has become an integral part of aircraft design. This assessment not only aids in monitoring pilots' states but also guides the optimization of cockpit display interfaces in avionics systems. By precisely adjusting display technologies, interface layouts, information presentation, and control arrangements, pilots can receive the most critical information at crucial moments, enhancing aviation safety. Additionally, real-time workload monitoringsystems can serve as auxiliary tools for pilot training, aiding pilots in understanding and adapting to workload patterns in different flight scenarios [4]. Methods for identifying workload primarily include task performance evaluation, subjective assessment, and physiological measurement [5]. Task performance evaluation and subjective assessment are post-task measurement methods conducted after task completion. In contrast, physiological measurement enables real-time workload monitoring by tracking fluctuations in physiological parameters, allowing the collection of operational data including the behavior patterns of pilots under various workload conditions. These real-time workload data can be used to optimize cockpit interface design, aligning it more closely with ergonomic principles and thereby enhancing flight safety. However, most methods based on physiological measurement rely on contact-based devices [6; 7; 8]. The foreign body sensation caused by wearing these physiological data collection devices significantly affects the participants' operational behavior. Previous studies on models based on physiological measurement have often used deep learning algorithms or neural networks, such as convolutional neural networks (CNNs) [9] and multimodal deep learning [10]. When cognitive workload is divided into low, medium, and high levels, the accuracy typically ranges from 65% to 80% [11; 12; 13], leaving room for improvement. During workload detection process, the feature data detected by the devices are usually stored in tabular form, and the inherent characteristics of these tabular feature data may be the reason for the limited accuracy of workload classification models [14]. Tabular feature data lack local features and have a weaker ability to integrate multiple data types; compared with text or image data, the number of features is relatively limited [15]. These factors make the processing of tabular feature data more complex, increasing the difficulty of developing accurate and reliable workload classification models. Additionally, obtaining sufficient experimental samples to train complex deep learning models through human-machine interaction experiments presents significant challenges. Previous studies typically involved approximately 20-30 participants, generating around 800-3000 samples [16; 17; 18]. Such a sample size may lead to overfitting and generalization issues, and it is necessary to augment the dataset. A major drawback of current data augmentation methods is the inability to ensure the quality of generated data, potentially leading to low-confidence samples that do not accurately represent real data distribution [19]. Therefore, there is an urgent need to establish a stable and reliable data augmentation method.