A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper proposes parameter-efficient fine-tuning (PEFT) methods for geospatial foundation models, enabling effective adaptation with minimal parameter updates.
Dan Zhang, Tao Feng, Lilong Xue, et al.
This survey provides a comprehensive overview of Parameter-Efficient Fine-Tuning (PEFT) methods for foundation models, categorizing and analyzing approaches to reduce computational costs while maintaining performance.
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This survey provides a comprehensive overview of parameter-efficient fine-tuning (PEFT) methodologies for large language models, addressing the computational challenges of full fine-tuning.
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Parameter-efficient fine-tuning (PEFT) achieves better performance and lower cost than in-context learning for few-shot tasks.
Zeyu Han, Chao Gao, Jinyang Liu, et al.
This survey comprehensively reviews parameter-efficient fine-tuning (PEFT) methods for large models, categorizing approaches and analyzing their trade-offs.