Optimization strategies and evaluation methods for fine-tuning large language models

This paper provides a comprehensive review of optimization strategies and evaluation methods for fine-tuning large language models. Key aspects such as the selection of loss function, optimizer, and learning rate tuning are discussed, along with evaluation methods including comparative experiments and specialized analysis techniques. Conclusions are drawn regarding the significance of fine-tuning in the development of NLP and its future directions.