Parameter Efficient Module

Parameter-efficient modules (PEMs) are lightweight components designed to adapt large pre-trained models to new tasks without retraining the entire network, thus significantly reducing computational costs and storage requirements. Current research focuses on improving the composition and application of these modules, exploring methods like weighted averaging of pre-trained PEMs, and automatically searching for optimal sparse structures within the models. This work is crucial for advancing efficient transfer learning across diverse applications, enabling more effective and scalable deployment of large language and vision models while mitigating issues like untruthfulness or toxicity.