Asymmetric Transfer in AI: Parameter-Efficient Fine-tuning Across Tasks and Languages

This ArXiv paper explores parameter-efficient fine-tuning methods, a crucial area for reducing computational costs and democratizing access to powerful language models. The research focuses on asymmetric transfer, potentially allowing for more efficient knowledge sharing between different tasks and languages.

• •Investigates the use of parameter-efficient fine-tuning techniques.
• •Focuses on asymmetric transfer, implying potential for efficient knowledge sharing.
• •Aims to optimize resource utilization in AI model training.