Summary of Mixture Of Lora Experts, by Xun Wu et al.
Mixture of LoRA Experts
by Xun Wu, Shaohan Huang, Furu Wei
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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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 proposes a new approach, Mixture of LoRA Experts (MoLE), to improve the fine-tuning of large pre-trained models for various downstream tasks. LoRA has become popular for its effectiveness and efficiency in fine-tuning large language models. However, existing methods for combining multiple LoRAs have limitations, such as losing generative capabilities or distinct identities. MoLE addresses these challenges by introducing hierarchical control and branch selection. Experimental evaluations in NLP and V&L domains show that MoLE outperforms direct arithmetic merging and retains flexibility in combining LoRAs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to combine multiple LoRA plugins to fine-tune large pre-trained models for different tasks. Right now, people use LoRA because it works well and is easy to use. But when we try to combine multiple LoRAs together, there are problems. This paper solves those problems by creating a new approach called MoLE. MoLE lets us control how the LoRAs work together and choose which ones to use. This makes it better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Nlp
