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Dynamic networks offer a strategy for making deep models like BERT more efficient during inference by adaptively altering the computation based on the input. This paradigm includes methods like depth-adaptive models, which dynamically select an optimal number of layers for processing a token and then skip the remainder of the layer stack. Another example is length-adaptive models, where the input sequence length is adjusted by skipping less important tokens to reduce the computational burden.

Dynamic Networks for Efficient BERT Inference

Depth-adaptive models are a type of dynamic network that improves BERT's inference efficiency. The core principle is to dynamically determine the optimal number of layers required to process a given token. The model can then exit early from an intermediate layer, skipping the remaining layers in the stack to reduce computation.

Depth-Adaptive BERT Models

Length-adaptive models are a strategy to enhance BERT's efficiency by dynamically adjusting the length of the input sequence during processing. This is accomplished by identifying and skipping less important tokens, thereby reducing the model's overall computational load.

Length-Adaptive BERT Models

A team of engineers is tasked with optimizing a large language model for real-time text summarization of news articles. They observe that the model's processing time is a major bottleneck. To address this, they implement a mechanism that, for each article, dynamically decides to skip processing certain less-informative sentences entirely, thereby reducing the total amount of text fed through the model's most computationally expensive components. Which principle of efficient model inference does 

Based on the case study, which dynamic network strategy (adjusting processing depth or adjusting input length) would be more appropriate to implement for improving efficiency? Justify your choice by explaining how the strategy addresses the specific problem described.

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