Summary of Bidora: Bi-level Optimization-based Weight-decomposed Low-rank Adaptation, by Peijia Qin et al.
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
by Peijia Qin, Ruiyi Zhang, Pengtao Xie
First submitted to arxiv on: 13 Oct 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 This paper proposes BiDoRA, a bi-level optimization-based parameter-efficient fine-tuning (PEFT) method for large language models (LLMs). BiDoRA improves upon weighted decomposed low-rank adaptation (DoRA) by optimizing the magnitude and direction components on separate datasets at different levels, reducing overfitting risk. The asynchronous optimization of these components allows for more flexible gradient updates suitable for various downstream tasks. Evaluations on fourteen datasets demonstrate BiDoRA’s superior performance compared to DoRA and other PEFT methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research improves a way to train large language models (LLMs) by making it more efficient and effective. The new method, called BiDoRA, works better than the previous one because it optimizes two parts of the model’s weights separately. This helps prevent overfitting and makes the model more adaptable to different tasks. Testing on various datasets shows that BiDoRA outperforms other methods. |
Keywords
» Artificial intelligence » Fine tuning » Low rank adaptation » Optimization » Overfitting » Parameter efficient
