Summary of Reslora: Identity Residual Mapping in Low-rank Adaption, by Shuhua Shi et al.
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
by Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 ResLoRA framework improves the low-rank adaptation (LoRA) method for fine-tuning large language models (LLMs). LoRA is a popular parameter-efficient fine-tuning approach, but updating its weights effectively and efficiently is challenging due to the long calculation path in the original model. ResLoRA addresses this by adding residual paths during training and using merging approaches to eliminate these extra paths during inference. The method achieves better results in fewer training steps without any extra trainable parameters or inference cost compared to LoRA. Experiments on NLG, NLU, and text-to-image tasks demonstrate its effectiveness. To the best of our knowledge, ResLoRA is the first work that combines the residual path with LoRA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ResLoRA is a new way to make large language models better at their job. These models are like super smart computers that can understand and generate human-like text. The problem is that making these models do more tasks takes a long time and uses too much computer power. ResLoRA makes it faster and cheaper by adding special paths in the model’s brain during training, and then removing them when the model is working on new tasks. This helps the model learn faster and be better at many tasks like writing stories or generating images from text. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Lora » Low rank adaptation » Parameter efficient
