Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies
This is an incremental review article summarizing existing methods for researchers and practitioners working with large language models.
The paper provides a comprehensive review of fine-tuning strategies for large language models, analyzing advancements in methods like task-adaptive fine-tuning and parameter-efficient fine-tuning, but does not present new experimental results or concrete numbers.
With the surge of ChatGPT,the use of large models has significantly increased,rapidly rising to prominence across the industry and sweeping across the internet. This article is a comprehensive review of fine-tuning methods for large models. This paper investigates the latest technological advancements and the application of advanced methods in aspects such as task-adaptive fine-tuning,domain-adaptive fine-tuning,few-shot learning,knowledge distillation,multi-task learning,parameter-efficient fine-tuning,and dynamic fine-tuning.