Understanding the Hidden Layers in Large Language Models LLMs
Practical Solutions and Value
Hebrew University Researchers conducted a study to understand the flow of information in large language models (LLMs) and found that higher layers rely less on the detailed representation of previous tokens. This offers potential optimizations, such as skipping attention in these layers to reduce computational costs.
One technique involves introducing noise by replacing hidden states with random vectors, allowing researchers to evaluate the importance of these hidden states at certain layers. Another method, freezing, locks the hidden states at a particular layer and reuses them for subsequent layers, reducing the computational load.
The study reveals a two-phase process in transformer-based LLMs: the early layers gather information from previous tokens, while the higher layers primarily process that information internally. This insight can lead to more informed and efficient model designs.
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