Low-Rank Compression of Language Models via Differentiable Rank Selection

This article announces research on compressing language models using low-rank approximation techniques. The core innovation appears to be a differentiable method for selecting the optimal rank, which is a key parameter in low-rank compression. This suggests potential improvements in model efficiency and resource utilization.

• •Focuses on compressing language models.
• •Employs low-rank approximation.
• •Introduces a differentiable rank selection method.
• •Aims to improve model efficiency.