2025
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
EMNLP 2025
The full-size MLPs and the projection layers in attention introduce tremendous model sizes of large language models (LLMs), consuming extensive computational resources in pre-training. We empirically observe that the activations of pre-trained LLMs exhibit low-rank property. Motivated by such observ