Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies

2024/04/13 by Weng, Benjue · 2 citations
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2404.09022

Abstract

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.

Cited by

• Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
• Enabling Precise Topic Alignment in Large Language Models Via Sparse Autoencoders

Related

• Large Language Models: A Survey
• Summary of ChatGPT-Related research and perspective towards the future of large language models
• Training Data for Large Language Model
• Large Language Models Meet NLP: A Survey
• A Survey of Large Language Models