Memory-Augmented Large Language Models: Overcoming Catastrophic Forgetting in Continual Learning

Abstract-This paper proposes a novel strategy for mitigating catastrophic forgetting of lifelong learning via memory-augmented large language models. Coupling external memory modules with standard deep learning frameworks, our methodology enables the model to retain context information over long periods of time and retrieve such information, preventing previously learned facts from being overwritten by new input data. We demonstrate our approach on the real WikiText-103 dataset, with the results of our experiments showing an extensive improvement in the retention of long-term dependencies and overall model performance. Our findings suggest that memory augmentation is a promising means to enhance the resilience of language models in ever-changing, dynamic settings and laying the groundwork for more robust and adaptable continual learning systems.