Summary of Less Is More: Extreme Gradient Boost Rank-1 Adaption For Efficient Finetuning Of Llms, by Yifei Zhang et al.
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs
by Yifei Zhang, Hao Zhu, Aiwei Liu, Han Yu, Piotr Koniusz, Irwin King
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 eXtreme Gradient Boosting LoRA (XGBLoRA) framework bridges the gap between practical and theoretical performances of low-rank adaptations by leveraging ensemble learning. By iteratively learning and merging a sequence of LoRA adaptations, XGBLoRA refines model predictions and outperforms standard LoRA while maintaining computational efficiency. This novel approach achieves comparable performance to full fine-tuning with significantly fewer trainable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fine-tuning Large Language Models (LLMs) is crucial for adapting pre-trained models to downstream tasks. However, the enormous size of LLMs poses significant challenges. To solve this problem, a promising solution called LoRA has emerged. But there’s a gap between its practical performance and theoretical optimum. This paper proposes XGBLoRA, a novel framework that bridges this gap by using ensemble learning. It iteratively learns and merges multiple adaptations to refine model predictions. XGBLoRA outperforms standard LoRA while being more efficient. The results show that it consistently outperforms standard LoRA and achieves performance comparable to full fine-tuning with fewer parameters. |
Keywords
» Artificial intelligence » Extreme gradient boosting » Fine tuning » Lora
