Summary of Flexora: Flexible Low Rank Adaptation For Large Language Models, by Chenxing Wei et al.
Flexora: Flexible Low Rank Adaptation for Large Language Models
by Chenxing Wei, Yao Shu, Ying Tiffany He, Fei Richard Yu
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 Large Language Models (LLMs) have revolutionized AI by scaling model parameters, enhancing generalization abilities, and unlocking new capabilities. However, their performance in specific downstream tasks is often hindered by knowledge boundaries on those tasks. To address this limitation, the widely used Low-Rank Adaptation (LoRA) method has been introduced for fine-tuning. Despite its effectiveness, LoRA can underperform on certain tasks due to overfitting. To overcome this issue, we propose Flexora, a flexible low-rank adaptation method that automatically selects the most important layers needing fine-tuning to achieve optimal performance on various downstream tasks. This is achieved by framing the layer selection problem as a well-defined hyperparameter optimization (HPO) problem and addressing it using unrolled differentiation (UD). Our extensive experiments demonstrate Flexora’s ability to consistently improve over existing baselines, showcasing its effectiveness in practice. We also provide theoretical insights and ablation studies for a comprehensive understanding of Flexora. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are helping us make better AI systems by allowing them to learn more from big data. However, these models often struggle when asked to perform specific tasks because they haven’t been trained on those tasks before. One way to help them is by fine-tuning the models for each task. A popular method for doing this is called LoRA, but it has its own limitations. To overcome these limitations, we created a new method called Flexora that can automatically choose which parts of the model need fine-tuning for each task. This allows the model to learn from the data more effectively and make better decisions. We tested our method on many AI systems and tasks, and it consistently performed better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Hyperparameter » Lora » Low rank adaptation » Optimization » Overfitting
