PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective

Researchers have introduced PEFT-Arena, a framework for evaluating parameter-efficient finetuning (PEFT) methods from a stability-plasticity perspective, which assesses the trade-off between adapting to target tasks and retaining pretrained capabilities¹. This approach recognizes that PEFT, the standard method for adapting large language models, must balance task-specific accuracy with the preservation of existing knowledge. By examining the stability-plasticity dilemma, PEFT-Arena provides a more comprehensive understanding of PEFT's effectiveness. The framework's focus on stability and plasticity has significant implications for the development of more robust and adaptable language models. As state-aligned threat activity increasingly raises the stakes for language model security, the ability to finetune models while preserving their core capabilities becomes crucial. Therefore, PEFT-Arena's stability-plasticity perspective matters to practitioners seeking to develop more secure and reliable language models.