Summary of Malora: Mixture Of Asymmetric Low-rank Adaptation For Enhanced Multi-task Learning, by Xujia Wang et al.
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning
by Xujia Wang, Haiyan Zhao, Shuo Wang, Hanqing Wang, Zhiyuan Liu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 introduces Mixture of Asymmetric Low-Rank Adaptaion (MALoRA), a novel fine-tuning framework for multi-task learning. Building upon the success of LoRA-based methods, MALoRA leverages asymmetric optimization to reduce the number of trainable parameters by 30% to 48%, while increasing training speed by 1.2x and matching computational efficiency with single-task LoRA models. This approach addresses overfitting issues in high-rank configurations, enhancing performance stability. The paper demonstrates that MALoRA consistently outperforms baseline methods across diverse multi-task learning scenarios, showcasing its potential for improved adaptation of large language models (LLMs) to downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart AI model that can learn many things at once. But sometimes, it gets too good and starts to forget what it learned earlier. This paper introduces a new way to train these AI models so they can learn more efficiently and avoid forgetting important information. The new method is called MALoRA, and it’s designed to help the AI model focus on the most important tasks while reducing the amount of training data needed. By doing this, MALoRA helps the AI model learn faster and perform better than other methods. The paper shows that MALoRA works well in many different situations, making it a promising approach for improving how we use these powerful AI models. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Multi task » Optimization » Overfitting
