Step-by-Step Unmasking for Parameter-Efficient Fine-Tuning of Large Language Models

Aradhye Agarwal, Suhas Kamasetty Ramesh, Ayan Sengupta, Tanmoy Chakraborty

Abstract: Fine-tuning large language models (LLMs) on downstream tasks requires substantial computational resources. Selective-PEFT, a class of parameter-efficient fine-tuning (PEFT) methodologies, aims to mitigate these computational challenges by selectively fine-tuning only a small fraction of the model parameters. Although parameter-efficient, these techniques often fail to match the performance of fully fine-tuned models, primarily due to inherent biases introduced during parameter selection. Traditional selective-PEFT techniques use a fixed set of parameters selected using different importance heuristics, failing to capture parameter importance dynamically and often leading to suboptimal performance. We introduce ID3, a novel selective-PEFT method that calculates parameter importance continually, and dynamically unmasks parameters by balancing exploration and exploitation in parameter selection. Our empirical study on 16 tasks spanning natural language understanding, mathematical reasoning, and summarization demonstrates the effectiveness of our method compared to fixed-masking selective-PEFT techniques. We analytically show that ID3 reduces the number of gradient updates by a factor of two, enhancing computational efficiency. Since ID3 is robust to random initialization of neurons and operates directly on the optimization process, it is highly flexible and can be integrated with existing additive and reparameterization-based PEFT techniques such as Adapters and LoRA, respectively.1

External IDs:dblp:journals/tacl/AgarwalRSC25