Summary of Mor: Mixture Of Ranks For Low-rank Adaptation Tuning, by Chuanyu Tang et al.
MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
by Chuanyu Tang, Yilong Chen, Zhenyu Zhang, Junyuan Shang, Wenyuan Zhang, Yong Huang, Tingwen Liu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Mixture of Ranks (MoR), a novel approach to Low-Rank Adaptation (LoRA) that addresses existing challenges in aligning LoRA’s performance with full fine-tuning. MoR learns rank-specific information for different tasks based on input and efficiently integrates multi-rank information, reducing the learning difficulty of LoRA and enhancing its multi-task capabilities. The proposed framework equates the integration of multiple LoRAs to expanding the rank of LoRA, while mathematical transformations of low-rank components derive high-rank information. MoR achieves impressive results, with a 1.31% performance improvement using only 93.93% of parameters compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoR is a new way to make Low-Rank Adaptation (LoRA) work better for different tasks. It learns special things about each task and combines them in a smart way, making LoRA more powerful and easier to use. This helps LoRA be as good as fine-tuning, which takes a lot of data and computation. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation » Multi task
