Safe LoRA

Safe LoRA focuses on mitigating the safety risks associated with parameter-efficient fine-tuning of large language models (LLMs) using the Low-Rank Adaptation (LoRA) technique. Current research emphasizes developing robust LoRA variants for federated learning, multi-modal applications, and various downstream tasks, often incorporating techniques like alternating minimization, adaptive parameter allocation, and dropout regularization to improve performance and efficiency. This work is significant because it addresses the crucial need for safe and efficient LLM adaptation, enabling wider adoption of powerful LLMs across diverse applications while mitigating potential harms.