The field of large language models is moving towards more efficient fine-tuning methods, with a focus on parameter-efficient techniques that reduce computational and memory costs. Recent research has shown that these methods can achieve superior performance and robust generalization capabilities, while also addressing issues such as overfitting and compromised honesty. Noteworthy papers in this area include: Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning, which proposes a novel method to enhance mathematical reasoning capabilities. Fine-Tuned LLMs Know They Don't Know, which introduces a parameter-efficient approach to recover honesty in large language models. Learning from the Undesirable, which proposes a regularization scheme to mitigate overfitting issues when fine-tuning language models with limited data. TS-PEFT, which introduces a token-selective parameter-efficient fine-tuning approach with learnable threshold gating. GFT, which proposes a graph feature tuning method for efficient point cloud analysis.