Advanced Approaches in NLP and Security: Addressing Catastrophic Forgetting Through Continual Learning and Resolving Data Imbalance in Semi-supervised Settings

Abstract

In the rapidly evolving field of machine learning, particularly in applications demanding continual or sequential learning, the phenomenon of catastrophic forgetting poses a significant challenge. This issue occurs when a model, trained on new tasks, inadvertently loses information related to earlier learned tasks. Several innovative methodologies have been developed to address this problem without relying on traditional methods that often require additional memory or compromise privacy. One such approach is the introduction of calibration techniques that adjust both parameters and output logits to balance the preservation of old knowledge with the acquisition of new concepts, as exemplified in frameworks that incorporate Logits Calibration (LC) and Parameter Calibration (PC). These techniques ensure the retention of previously learned parameters while integrating new information, thereby maintaining performance across a variety of tasks, such as those in the General Language Understanding Evaluation (GLUE) benchmark. Another promising method involves the use of Energy-Based Models (EBMs), which associate an energy value with each input and allow the sampling of data points from previous tasks during new task training. This method has been adapted in different solutions, with the latter combining EBMs with Dynamic Prompt Tuning (DPT) to adaptively adjust prompt parameters for each task, efficiently generating training samples from past tasks and thus mitigating the effects of catastrophic forgetting. In the realm of cybersecurity, particularly in analyzing imbalanced, tabular data sets such as those encountered in industrial control systems and cybersecurity monitoring, semi- supervised learning techniques have been employed. These methods leverage a mix of labeled and unlabeled data and utilize novel data augmentation techniques triplet mixup to overcome the challenges posed by limited labeled data and the loss of contextual information. These approaches have demonstrated effectiveness in detecting vulnerabilities and attacks within cyber-physical systems, highlighting their potential in sectors where high stakes and high data imbalance are common. Across these diverse applications, the overarching goal remains consistent: to develop machine learning models capable of continual learning without sacrificing previously acquired knowledge. By harnessing innovative strategies such as parameter calibration, energy-based sampling, and semi-supervised learning with data augmentation, we are setting new benchmarks in the field, ensuring that models not only retain old knowledge but also seamlessly integrate new information, thereby paving the way for more robust, adaptive machine learning applications