ShortGPT: Layers in Large Language Models are More Redundant Than You
  Expect
Authors: name: Xin Men, name: Mingyu Xu, name: Qingyu Zhang, name: Bingning Wang, name: Hongyu Lin, name: Yaojie Lu, name: Xianpei Han, name: Weipeng Chen

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: layer removal, in which we directly
delete the redundant layers in LLMs based on their BI scores. Experiments
demonstrate that our method, which we call ShortGPT, significantly outperforms
previous state-of-the-art (SOTA) methods in model pruning. Moreover, ShortGPT
is orthogonal to quantization-like methods, enabling further reduction in
parameters and computation. The ability to achieve better results through
simple layer removal, as opposed to more complex pruning techniques, suggests a
high degree of redundancy in the model architecture.