The paper "Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications" by Charith Chandra Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, and Aman Chadha reviews the advancements in Parameter Efficient Fine-Tuning (PEFT) techniques. Traditional fine-tuning methods, which involve adjusting all parameters, are computationally and memory-intensive, leading to the development of PEFT methods that selectively update a subset of parameters to balance efficiency and performance. The review covers various applications, including text generation, medical imaging, protein modeling, and speech synthesis, highlighting the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage. Key techniques such as Low-Rank Adaptation (LoRA) and Differentiable Rank Adaptation (DoRA) are discussed, with LoRA showing superior performance in certain tasks. The paper also addresses challenges such as balancing efficiency and performance, data scarcity, overfitting, and the capacity constraints of incremental modules. Future research directions include task-agnostic PEFT techniques, privacy-preserving PEFT, enhancing robustness with limited labeled data, and improving interpretability of fine-tuned models. Overall, the paper aims to contribute to the democratization of deep learning by making it more accessible and adaptable across a wide range of applications.The paper "Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications" by Charith Chandra Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, and Aman Chadha reviews the advancements in Parameter Efficient Fine-Tuning (PEFT) techniques. Traditional fine-tuning methods, which involve adjusting all parameters, are computationally and memory-intensive, leading to the development of PEFT methods that selectively update a subset of parameters to balance efficiency and performance. The review covers various applications, including text generation, medical imaging, protein modeling, and speech synthesis, highlighting the effectiveness of PEFT methods in reducing computational load, speeding up training, and lowering memory usage. Key techniques such as Low-Rank Adaptation (LoRA) and Differentiable Rank Adaptation (DoRA) are discussed, with LoRA showing superior performance in certain tasks. The paper also addresses challenges such as balancing efficiency and performance, data scarcity, overfitting, and the capacity constraints of incremental modules. Future research directions include task-agnostic PEFT techniques, privacy-preserving PEFT, enhancing robustness with limited labeled data, and improving interpretability of fine-tuned models. Overall, the paper aims to contribute to the democratization of deep learning by making it more accessible and adaptable across a wide range of applications.