Meta-Learning Approaches for Efficient Adaptation of Large Language Models

This article explores how meta-learning can improve the efficiency of adapting large language models to new tasks using limited data and computational resources. It examines approaches such as few-shot learning, LoRA, and prompt-based adaptation, highlighting their benefits, challenges, and potential for developing more flexible and cost-effective AI systems.