LoRA works by adding small, low-rank weight matrices to a pre-trained model instead of updating all parameters—signal processing principles can guide better design choices for this approach and similar efficient fine-tuning methods.
This paper examines LoRA (Low-Rank Adaptation), a widely-used technique for efficiently fine-tuning large AI models, through the lens of signal processing. It explains the core mechanisms behind LoRA variants and how classical signal processing tools can improve parameter-efficient fine-tuning methods, covering architectural design, optimization strategies, and real-world applications.