Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models
This addresses the problem of optimizing fine-tuning for large language models, offering a novel method that enhances task adaptation while maintaining inference efficiency.
The paper tackles the problem of fine-tuning large language models by proposing Linear Chain Transformation (LinChain), which introduces a sequence of linear transformations during fine-tuning to enrich optimization dynamics. The result shows that LinChain significantly improves performance over state-of-the-art methods, leading to better generalization, fewer learnable parameters, and improved task adaptation on various benchmark tasks.
Fine-tuning large language models (LLMs) has become essential for adapting pretrained models to specific downstream tasks. In this paper, we propose Linear Chain Transformation (LinChain), a novel approach that introduces a sequence of linear transformations during fine-tuning to enrich optimization dynamics. By incorporating multiple linear transformations into the parameter update process, LinChain expands the effective rank of updates and enhances the model's ability to learn complex task-specific representations. We demonstrate that this method significantly improves the performance of LLM fine-tuning over state-of-the-art methods by providing more flexible optimization paths during training, while maintaining the inference efficiency of the resulting model. Our experiments on various benchmark tasks show that LinChain leads to better generalization, fewer learnable parameters, and improved task adaptation, making it a compelling strategy for LLM fine-tuning.