Summary of Lora Done Rite: Robust Invariant Transformation Equilibration For Lora Optimization, by Jui-nan Yen et al.
LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization
by Jui-Nan Yen, Si Si, Zhao Meng, Felix Yu, Sai Surya Duvvuri, Inderjit S. Dhillon, Cho-Jui Hsieh, Sanjiv Kumar
First submitted to arxiv on: 27 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 LoRA-RITE, a novel adaptive matrix preconditioning method for LoRA optimization, which achieves transformation invariance and remains computationally efficient. The method is designed to improve the performance of LoRA fine-tuning, particularly for large language models (LLMs). The authors provide theoretical analysis and experimental results demonstrating the effectiveness of LoRA-RITE on various LLM tasks with different models, including Gemma 2B, 7B, and mT5-XXL. For example, replacing Adam with LoRA-RITE during fine-tuning of Gemma-2B resulted in a 4.6% accuracy gain on Super-Natural Instructions and 3.5% accuracy gains across four LLM benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRA-RITE is a new way to make language models better by improving how they learn. The old way was not perfect, so the researchers created this new method to fix the problem. They tested it with different types of models and showed that it works really well. For example, when they used LoRA-RITE with Gemma-2B, it got 4.6% better at understanding natural language instructions. It also did better on other tasks like answering questions and generating text. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Optimization
