arXiv:2404.09022
공식 코드 · Papers with Code
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies
원문 초록
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.
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CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
Robust-Multi-Task Gradient Boosting
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
토론
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