Continual Instruction Tuning

Continual instruction tuning (CIT) focuses on adapting large language models (LLMs) to new tasks sequentially, without catastrophic forgetting of previously learned skills. Current research emphasizes mitigating this forgetting through techniques like data replay, model expansion, and parameter-efficient tuning methods, often applied to both unimodal and multimodal LLMs. This area is crucial for developing more robust and adaptable AI systems, enabling them to continuously learn and improve in dynamic real-world environments, and addressing limitations in current instruction tuning approaches.