The field of large language models (LLMs) is rapidly evolving, with a focus on developing more efficient and effective fine-tuning methods. Recent research has centered around parameter-efficient fine-tuning (PEFT) techniques, which aim to adapt pre-trained LLMs to downstream tasks without requiring extensive retraining. A key direction in this area is the development of low-rank adaptation (LoRA) methods, which enable fine-tuning through low-rank, factorized weight matrices. Another important trend is the use of hypernetworks, which can generate context-aware LoRA adapters from textual descriptions, allowing for more efficient and scalable LLM personalization. Furthermore, researchers are exploring the use of sparse fine-tuning techniques, such as GaLLoP, which fine-tune only the most task-relevant parameters, mitigating catastrophic forgetting and memorization of task data. Noteworthy papers in this area include: CTR-LoRA, which introduces a curvature-aware and trust-region guided LoRA framework, achieving consistent improvements over strong PEFT baselines. Long Exposure, which proposes an efficient system to accelerate PEFT for LLMs under shadowy sparsity, offering up to a 2.49x speedup in end-to-end fine-tuning. Instant Personalized Large Language Model Adaptation via Hypernetwork, which enables instant adaptation, generalization to unseen users, and privacy-preserving local deployment. Zhyper, which achieves competitive performance with up to 26x fewer parameters than state-of-the-art baselines. GaLLoP, which consistently improves or matches the in-distribution and out-of-distribution performance obtained via leading PEFT techniques.