Summary of Initialization Using Update Approximation Is a Silver Bullet For Extremely Efficient Low-rank Fine-tuning, by Kaustubh Ponkshe et al.
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
by Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov, Samuel Horvath, Praneeth Vepakomma
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes a method called LoRA Silver Bullet (LoRA-SB) to efficiently fine-tune large language models (LLMs). The approach approximates full fine-tuning within low-rank subspaces using a carefully designed initialization strategy. The authors theoretically demonstrate that the architecture of LoRA-XS provides the precise conditions needed for this approximation, allowing for optimal scaling and hyperparameter tuning-free updates. Experiments across various tasks, including mathematical reasoning, commonsense reasoning, and language understanding, show that LoRA-SB outperforms standard LoRA while using significantly fewer learnable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRA Silver Bullet is a new way to fine-tune big language models without losing performance. It uses a special initialization strategy to get the right updates in low-rank spaces. This means it can be faster and use less memory than traditional methods, but still achieve good results. The authors tested LoRA-SB on different tasks and showed that it works better than other methods while using 27-90 times fewer parameters. |
Keywords
» Artificial intelligence » Fine tuning » Hyperparameter » Language understanding » Lora
