LoRA Module

Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning technique for large language and vision models, aiming to improve performance on specific tasks without retraining the entire model. Current research focuses on enhancing LoRA's capabilities through architectural modifications, such as incorporating multiple LoRA modules for multi-task learning (TeamLoRA, Mixture-of-LoRAs), modality-specific adaptations (Lateralization LoRA), and cascaded learning strategies (LoRASC) to improve expressiveness and generalization. These advancements address limitations in existing LoRA implementations, such as overfitting and limited expressiveness, leading to more efficient and effective fine-tuning for various applications, including image generation and retrieval-augmented generation.