This reviews the paper which introduces Low-Rank Adaptation (LoRA), a novel method designed to efficiently adapt large language models for specific downstream tasks. Traditional fine-tuning, which retrains all model parameters, becomes prohibitively expensive for models like GPT-3 with billions of parameters. LoRA addresses this by freezing the pre-trained weights and injecting small, trainable rank decomposition matrices into the Transformer architecture, significantly reducing the number of trainable parameters and GPU memory requirements. This approach matches or exceeds the performance of full fine-tuning while offering faster training, lower storage costs, and no additional inference latency. The research also explores the optimal low rank for adaptation and the relationship between the original model weights and the learned low-rank updates.

Source: https://arxiv.org/pdf/2106.09685