Summary of Parameter-efficient Fine-tuning Via Circular Convolution, by Aochuan Chen et al.
Parameter-Efficient Fine-Tuning via Circular Convolution
by Aochuan Chen, Jiashun Cheng, Zijing Liu, Ziqi Gao, Fugee Tsung, Yu Li, Jia Li
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary LoRA, a popular method for fine-tuning large foundation models, uses low-rank matrices A and B to represent weight changes (ΔW = BA). This approach reduces trainable parameters and alleviates memory consumption issues by sequentially multiplying A and B with the activation. Despite its success, LoRA’s intrinsic low-rank characteristic may limit its performance. The paper proposes Circular Convolution Adaptation (C3A), which achieves high-rank adaptation with enhanced performance while maintaining computational power and memory utilization. C3A consistently outperforms LoRA and its variants across various fine-tuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have developed a way to make large AI models more accurate by changing small parts of the model’s weights. This method, called LoRA, uses special matrices to represent these changes. While it works well, there might be limitations due to its design. The paper proposes a new approach, C3A, which improves upon LoRA by making the changes in a different way that is more powerful and efficient. Tests show that C3A performs better than LoRA on various tasks. |
Keywords
» Artificial intelligence » Fine tuning » Lora
