Low-Rank Adaptation with Task-Relevant Feature Enhancement for Fine-tuning Language Models
This work addresses the problem of parameter-efficient fine-tuning for language models, offering an incremental improvement over existing low-rank methods.
The paper tackles the performance gap between LoRA and full fine-tuning in language models by proposing LoRATRF, which enhances task-relevant features, reducing parameters by 33.71% and achieving better performance on various datasets.
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. LoRA is one of the most widely used methods, which assumes that the optimization process is essentially low dimensional. Although LoRA has demonstrated commendable performance, there remains a significant performance gap between LoRA and full fine-tuning when learning new tasks. In this work, we propose Low-Rank Adaptation with Task-Relevant Feature Enhancement(LoRATRF) for enhancing task-relevant features from the perspective of editing neural network representations. To prioritize task-relevant features, a task-aware filter that selectively extracts valuable knowledge from hidden representations for the target or current task is designed. As the experiments on a vareity of datasets including NLU, commonsense reasoning and mathematical reasoning tasks demonstrates, our method reduces 33.71% parameters and achieves better performance on a variety of datasets in comparison with SOTA low-rank methods.