DoRA: Weight-Decomposed Low-Rank Adaptation (AI summary)

Key Points

1. The paper introduces Weight-Decomposed Low-Rank Adaptation (DoRA) as a parameter-efficient fine-tuning method compatible with LoRA and its variants, aiming to bridge the accuracy gap between them and full fine-tuning (FT).

2. The authors propose a weight decomposition analysis to uncover the differences in learning patterns between FT and various parameter-efficient fine-tuning methods, and based on these findings, they introduce DoRA to resemble the learning capacity of FT.

3. DoRA decomposes pretrained weights into magnitude and directional components and efficiently updates the directional component using LoRA, showing enhanced learning capacity and stability without introducing additional inference overhead.

4. Experimental results demonstrate that DoRA consistently outperforms LoRA across various downstream tasks, including commonsense reasoning, visual instruction tuning, and image/video-text understanding.

5. DoRA is designed to address the challenge of training large-scale models by reducing the expense associated with fine-tuning the entire model through its weight decomposition and adaptation method, achieving a learning capacity closely resembling full fine-tuning without additional inference latency.

6. The paper includes an analysis of the differences in learning patterns of weight updates between DoRA and FT compared to LoRA, attributing the superior performance of DoRA to its ability to make substantial directional adjustments with minimal changes in magnitude, similar to FT.

7. DoRA is evaluated on various tasks, including language, image, and video domains, and consistently demonstrates superior performance compared to LoRA and other parameter-efficient fine-tuning methods, showcasing its efficacy and compatibility with different types of downstream tasks.

8. The paper also presents DoRA's compatibility with another parameter-efficient fine-tuning method, VeRA, demonstrating its ability to enhance learning capacity and accuracy while reducing the number of trainable parameters.

9. The findings suggest that DoRA can be a promising and efficient alternative to LoRA, with potential applications in various domains such as language, vision, and beyond, including the exploration of its potential in audio-related tasks.

Summary

The paper introduces a novel weight decomposition analysis to investigate the differences between parameter-efficient finetuning (PEFT) methods, particularly LoRA, and full fine-tuning (FT). The authors propose Weight-Decomposed Low-Rank Adaptation (DoRA) to bridge the accuracy gap, enhance learning capacity, and training stability of LoRA while avoiding additional inference overhead. DoRA decomposes the pre-trained weights into magnitude and directional components, thereby improving learning capacity and training stability.

The researchers evaluated the performance of DoRA compared to LoRA on various downstream tasks, such as commonsense reasoning, visual instruction tuning, and image/video-text understanding. Their findings show that DoRA consistently outperforms LoRA across these tasks, thus demonstrating its potential to enhance the learning capability of LoRA.

Additionally, the paper explores the compatibility of DoRA with other LoRA variants, showing its resilience and consistently superior performance. The work also delves into the impact of different rank configurations on DoRA and LoRA and explores DoRA's robustness under varying degrees of training data, showcasing the method's consistent improvement over LoRA and other variants. Overall, the findings suggest that DoRA is a promising method for enhancing the performance of parameter-efficient fine-tuning methods compared to traditional full fine-tuning approaches.

The paper's contributions include introducing DoRA as a novel PEFT method that incorporates weight decomposition to achieve a learning capacity closely resembling FT without any additional inference latency over LoRA. Additionally, the authors proposed a novel weight decomposition analysis to uncover the fundamental differences in the learning patterns of FT and different PEFT methods. DoRA consistently surpasses LoRA on various tasks, from NLP to Vision-Language benchmarks and across various backbones, including LLM and LVLM. The paper concludes with plans for future work to explore the generalizability of DoRA in domains beyond language and vision, such as in the field of audio, and to investigate the potential of DoRA in various other applications.

Reference: https://arxiv.org/abs/2402.093...