ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

**Paper**: [https://arxiv.org/abs/2403.03853](https://arxiv.org/abs/2403.03853)

**Abstract**:

>As Large Language Models (LLMs) continue to advance in performance, their size has escalated significantly, with current LLMs containing billions or even trillions of parameters. However, in this study, we discovered that many layers of LLMs exhibit high similarity, and some layers play a negligible role in network functionality. Based on this observation, we define a metric called **Block Influence** (**BI**) to gauge the significance of each layer in LLMs. We then propose a straightforward pruning approach: …

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