The field of natural language processing is witnessing significant advancements in efficient fine-tuning methods for large language models. Researchers are exploring innovative approaches to adapt these models to specialized tasks while minimizing computational costs and preserving previously learned knowledge. A notable direction is the development of parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA) and its variants, which enable efficient adaptation of large models with reduced trainable parameters. Another area of focus is the combination of LoRA with Mixture-of-Experts (MoE) architectures, allowing for enhanced capacity and improved performance. Furthermore, scientists are investigating techniques to mitigate task conflict and oblivion in multi-task scenarios, ensuring that adapted models retain their original capabilities while acquiring new knowledge. Noteworthy papers in this area include MoTE, which proposes a mixture of task-specific experts framework to address dimensional inconsistency in class-incremental learning, and GenFT, which introduces a generative parameter-efficient fine-tuning method to extract structured information from pre-trained weights. Additionally, papers like D-MoLE and EMLoC demonstrate the effectiveness of dynamic mixture of experts and emulator-based fine-tuning methods in achieving state-of-the-art performance while reducing computational requirements. Overall, these developments are paving the way for more efficient, flexible, and scalable natural language processing systems.