Summary of Sketch to Adapt: Fine-tunable Sketches For Efficient Llm Adaptation, by Tianyi Zhang et al.
Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation
by Tianyi Zhang, Junda Su, Aditya Desai, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 SketchTune method is a parameter-efficient fine-tuning technique for large language models (LLMs) that leverages sketching to compress and adapt model weights. This approach eliminates the need for low-rank assumptions, allowing for faster and more memory-efficient training and inference. The integration of compression and adaptation into a unified framework enables the use of smaller base models while maintaining comparable trainable parameters. The method is supported by mathematical insights into matrix classes that are better approximated using sketching rather than low-rank methods. Experimental results demonstrate that SketchTune outperforms leading PEFT methods across diverse tasks, including math problem-solving, common sense reasoning, and instruction following. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) can be adapted for specific tasks by fine-tuning their weights. However, this process is challenging due to the enormous size of these models. To make adaptation more efficient, researchers have developed parameter-efficient fine-tuning techniques that use additive adapters applied to frozen model weights. Sketching is a new approach that compresses LLM weights into compact fine-tunable sketches, allowing for faster and more memory-efficient training and inference. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Parameter efficient
