META-CONTINUAL ADAPTATION IN LARGE LANGUAGE MODELS FOR ROBUST CROSS-DOMAIN GENERALIZATION

Abstract

The ability of large language models (LLMs) to generalize across diverse domains remains a significant challenge in natural language processing. While LLMs have achieved remarkable success on specific tasks, their performance often deteriorates when applied to data from different domains due to their limited capacity for cross-domain adaptation. In this paper, we propose a novel approach for improving domain generalization by combining meta-learning and continual learning techniques, which we refer to as Meta-Continual Adaptation. This method leverages the strengths of metalearning to enable LLMs to quickly adapt to new domains while simultaneously employing continual learning to prevent catastrophic forgetting when transitioning between tasks. We demonstrate the efficacy of our approach through extensive experiments on multiple cross-domain benchmarks, showing that Meta-Continual Adaptation significantly enhances the robustness and generalization ability of LLMs compared to traditional methods. Our results reveal that this approach not only improves the performance of LLMs across diverse domains but also reduces the need for extensive retraining, making it more efficient and scalable for real-world applications. Finally, we discuss potential avenues for future research, including the integration of unsupervised data and further optimization of learning strategies.