Summary of Chain Of Lora: Efficient Fine-tuning Of Language Models Via Residual Learning, by Wenhan Xia et al.
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning
by Wenhan Xia, Chengwei Qin, Elad Hazan
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper tackles a crucial problem in machine learning: fine-tuning large language models to specific tasks efficiently. The authors focus on parameter-efficient methods, particularly the low-rank adaptation (LoRA) family, which is widely used due to its advantages. However, LoRA has limitations, and the study explores how it compares to full-parameter fine-tuning for certain tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make computers better at understanding language by finding ways to train big models quickly and accurately. Researchers use a technique called LoRA (low-rank adaptation) to adapt these large models to specific tasks without using all the model’s parameters. This method is popular because it works well, but sometimes it doesn’t do as well as using the entire model. The paper investigates why this is the case. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Low rank adaptation * Machine learning * Parameter efficient
