<ul><li>The rapid development of large language models has led to the need for efficient fine-tuning methods to overcome computational limitations.</li><li>In response to this, a new framework called MAP has been proposed, aiming to improve the efficiency and interpretability of weight adaptation in pre-trained models.</li><li>MAP decouples weight adaptation into direction and magnitude components in a rigorous manner, allowing for more flexible and interpretable adaptation processes.</li><li>Experiments have shown that MAP can significantly enhance the performance of existing parameter-efficient fine-tuning methods, making it a valuable addition to the field.</li></ul>

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation

Discover more