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Evaluating Model Compression Strategies
A development team needs to deploy a large, pre-trained Transformer-based language model on a mobile device with limited processing power. Their primary goal is to significantly reduce inference time while minimizing the loss of accuracy on a sentiment analysis task. They are considering two different strategies for compressing the model. Evaluate the two proposals below and justify which one is more likely to achieve the team's goal.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Evaluation in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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Evaluating Model Compression Strategies
A development team needs to accelerate the inference speed of a large, pre-trained language model for a task requiring a deep understanding of long-range dependencies and complex sentence structures. Which of the following strategies for reducing the model's size is most likely to severely degrade performance on this specific task?
A machine learning team is exploring different methods to reduce the size and inference time of a large language model based on the Transformer architecture. Match each pruning technique with its most likely description or primary impact.