The Unreasonable Ineffectiveness of the Deeper Layers
Andrey Gromov Kushal Tirumala Hassan Shapourian Paolo Glorioso Dan Roberts
How is knowledge stored in an LLM’s weights? We study this via layer pruning: if removing a certain layer does not affect model performance in common question-answering benchmarks, then the weights in that layer are not necessary for storing the knowledge needed to answer those questions. To find these unnecessary parameters, we identify the optimal block of layers to prune by considering similarity across layers; then, to “heal” the damage, we perform a small amount of finetuning. Surprisingly, with this method we find minimal degradation of performance until after a large fraction (up to half) of the layers are removed for some common open-weight models. From a scientific perspective, the robustness of these LLMs to the deletion of layers implies either that current pretraining methods are not properly leveraging the parameters in the deeper layers of the network or that the shallow layers play a critical role in storing knowledge. For our study, we use parameter-efficient finetuning (PEFT) methods, specifically quantization and Low Rank Adapters (QLoRA), such that each of our experiments can be performed on a single 40GB A100 GPU.
NLP, Pruning, Science of Deep Learning, Efficient Inference
We use model pruning as tool to understand how and where knowledge is located in open-weight LLMs: we find that we can remove up to half the layers of Llama-2 70B with essentially no impact on performance on QA benchmarks.