Microsoft AI researchers have developed ResLoRA, an enhanced framework for Low-Rank Adaptation (LoRA). It introduces residual paths during training and employs merging approaches for path removal during inference. Outperforming original LoRA and baseline methods, ResLoRA achieves superior outcomes across Natural Language Generation (NLG), Natural Language Understanding (NLU), and text-to-image tasks.
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Microsoft AI Researchers Developed a New Improved Framework ResLoRA for Low-Rank Adaptation (LoRA)
Introduction
Large language models (LLMs) with hundreds of billions of parameters have significantly improved performance on various tasks. Fine-tuning LLMs on specific datasets enhances performance compared to prompting during inference but incurs high costs due to parameter volume. Low-rank adaptation (LoRA) is a popular parameter-efficient fine-tuning method for LLMs, yet updating LoRA block weights efficiently is challenging due to the model’s long calculation path.
ResLoRA: Enhanced Framework for LoRA
Researchers from the School of Computer Science and Engineering, Beihang University, Beijing, China, and Microsoft have introduced ResLoRA, an improved framework of LoRA. ResLoRA mainly consists of two parts: ResLoRA blocks and merging approaches. ResLoRA blocks add residual paths to LoRA blocks during training, while merging approaches convert ResLoRA to LoRA blocks during inference. Researchers also claimed that, to their knowledge, ResLoRA is the first work that combines the residual path with LoRA.
Key Features and Benefits
ResLoRA introduces residual paths during training and employs merging approaches for path removal during inference. It outperforms original LoRA and other baseline methods across natural language generation (NLG), natural language understanding (NLU), and text-to-image tasks. The results confirm ResLoRA’s effectiveness, achieving superior outcomes with fewer training steps and no additional trainable parameters.
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Conclusion
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