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insight - Fine-tuning Strategies for Large Language Models
Two-Stage LLM Fine-Tuning: Addressing Format Specialization for Improved Generalization
Addressing format specialization during fine-tuning improves generalization in large language models.
Comprehensive Review and Analysis of Fine-Tuning Strategies for Large Language Models
This paper provides a comprehensive review and analysis of various fine-tuning strategies for adapting large language models to specific tasks and domains, including 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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