Summary of Modula: Mixture Of Domain-specific and Universal Lora For Multi-task Learning, by Yufei Ma et al.
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning
by Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen, Dehong Gao, Zhuoran Ran, Wang Zihan, Linbo Jin, Wen Jiang, Guannan Zhang, Xiaoyan Cai, Libin Yang
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed MoDULA (Mixture of Domain-Specific and Universal LoRA) paradigm is a novel fine-tuning method that improves the multi-task capability of Large Language Models (LLMs). It does this by training universal experts, domain-specific experts, and routers separately, allowing for efficient fine-tuning and parameter efficiency in multi-task learning. The MoDULA-Res method within this paradigm maintains the model’s general capability through residual connections between universal and task-specific experts. Experimental results show that MoDULA-Flan and MoDULA-Res outperform existing fine-tuning methods on various LLMs, with MoDULA-Res achieving significant performance improvements while reducing training costs by over 80%. The approach also allows for the efficient addition of new tasks without retraining existing experts from scratch. Overall, MoDULA provides a scalable and cost-effective solution for fine-tuning LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoDULA is a way to make Large Language Models better at doing many things at once. It’s like having a special tool that helps the model learn new tasks without getting too confused or taking too long. The tool uses separate parts of the model, each one good at different things. This makes it possible for the model to get really good at lots of tasks while still being able to understand language in general. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Multi task
