Addressing format specialization during fine-tuning improves generalization in large language models.
This paper provides a comprehensive review and analysis of various fine-tuning strategies for adapting large language models to specific tasks and domains, including task-adaptive fine-tuning, domain-adaptive fine-tuning, few-shot learning, knowledge distillation, multi-task learning, parameter-efficient fine-tuning, and dynamic fine-tuning.