Pruning for BERT Compression

Quantization for BERT Compression

A development team is working to optimize a large, pre-trained language model for a real-time translation application. The model's current inference speed is too slow. They are considering two strategies: (1) removing a specific number of attention heads from each layer, or (2) representing all model parameters with lower-precision numbers. Which statement best distinguishes the primary impact of these two compression techniques in this context?

BERT Compression Strategy for Mobile Deployment

An engineering team needs to make a large language model more efficient for deployment. They are considering two distinct compression methods. Match each method with its corresponding description.