Task Specific Structured Pruning

Task-specific structured pruning aims to reduce the size and computational cost of large language models (LLMs) and other deep learning models, such as those used in speech recognition and vision-language tasks, without significant performance loss. Current research focuses on developing efficient algorithms that prune model components like attention heads, neurons, or entire layers, often incorporating techniques like knowledge distillation or sparse regularization to mitigate performance degradation. These advancements are crucial for deploying large models on resource-constrained devices and reducing the environmental impact of training and inference, impacting both research efficiency and practical applications.