This article is part of a series of LLM articles in response to Navigating the Landscape of Large LanguageModels: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies》translations。
Handling largelanguage model: A review of the paradigm andtrimmingComprehensive review and analysis of strategies
- summaries
- 1 Introduction
- 2 Related work
- 3 Transformer Architecture
- 4 LLMs paradigm
- 5 Task-specific adaptation fine-tuning
- 6 Small sample learning and meta-learning
- 7 Knowledge distillation and transfer learning
- 8 Multi-task learning
- 9 Efficient fine-tuning of parameters
- 10 Command Adjustment
- 11 Alignment adjustments
- 12 Agent Adjustment
- 13 RAG Memory Trim
- 14 Experimental
- 15 Challenges and future directions
- 16 Conclusion
summaries
in the wake ofChatGPTof the surge in the use of large models has increased significantly, rising rapidly throughout the industry and sweeping thethe Internet. This paper provides a comprehensive review of fine-tuning methods for large-scale models. This paper investigates the latest technological advances and applications of advanced methods in task adaptive fine-tuning, domain adaptive fine-tuning, small sample learning, knowledge distillation, multi-task learning, parameter efficient fine-tuning and dynamic fine-tuning.
1 Introduction
2 Related work
3 Transformerbuild
4 LLMs paradigm
5 Task-specific adaptation fine-tuning
6 Small sample learning and meta-learning
7 Knowledge distillation and transfer learning
8 Multi-task learning
9 Efficient fine-tuning of parameters
10 Command Adjustment
11 Alignment adjustments
12 Agent Adjustment
13 RAG Memory Trim
14 Experimental
15 Challenges and future directions
16 Conclusion
This paper provides an overview of the advances in fine-tuning LLM, emphasizing different approaches such as task-specific adaptation, small-sample learning, and innovative techniques such as LoRA. It emphasizes the role of these models in enhancing language understanding and production in different domains. The paper concludes by pointing out promising directions for future research, especially in improving fine-tuning techniques to increase efficiency and effectiveness in handling complex NLP tasks.