LoRA, or Low-Rank Adaptation of Large Language Models, is a technique that reduces the number of trainable parameters for downstream tasks by freezing pre-trained model weights and injecting trainable rank decomposition matrices into each layer of the Transformer architecture. This approach greatly reduces the number of parameters required for fine-tuning, making it more feasible to deploy large language models in real-world applications. The authors argue that most existing fine-tuning methods are unattractive options, as they either introduce inference latency or result in a fine-tune model that doesn't compare strongly against the full baseline tuning. LoRA achieves better performance than these methods by representing the weight updates in a lower-dimensional space using matrix decomposition, specifically singular value decomposition (SVD). This approach allows for significant reduction in memory usage and training time. The authors demonstrate that LoRA can be used to fine-tune large language models on specific tasks, such as human language to SQL translation, with improved performance compared to existing methods. However, the technique has limitations, including the need to carefully select which adapter matrices to use and potential issues with stacking multiple adapters. Despite these challenges, LoRA has the potential to revolutionize the deployment of large language models in real-world applications by reducing the complexity and cost associated with fine-tuning.