Summary of Structured Unrestricted-rank Matrices For Parameter Efficient Fine-tuning, by Arijit Sehanobish et al.
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
by Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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 Recent advancements in scaling Transformer models have led to significant progress across various tasks. However, fine-tuning these large models for specific tasks is computationally expensive due to their massive parameter counts. To address this challenge, researchers have proposed parameter-efficient fine-tuning (PEFT) approaches that allow updating only a small number of parameters. This paper presents a general framework for PEFT using structured unrestricted-rank matrices (SURMs), which can be used as a drop-in replacement for popular methods like Adapters and LoRA. Unlike other methods, SURMs provide more flexibility in finding the right balance between compactness and expressiveness by utilizing low displacement rank matrices (LDRMs). The proposed approach achieves 5-7% accuracy gains on image classification tasks while reducing the parameter budget, often outperforming baselines. On the GLUE benchmark, SURMs result in up to a 12x reduction of parameters in adapters with minimal quality loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models more efficient and useful for specific tasks. Right now, these models are very powerful but take a lot of computer power to train. The authors propose a new way to fine-tune these models that uses fewer parameters while still getting good results. This approach is called SURM (Structured Unrestricted-Rank Matrix) and it’s like a super-powerful adapter that can learn from small amounts of data. It works really well on image classification tasks, getting 5-7% better accuracy while using much less computer power. On another important benchmark, GLUE, SURM reduces the number of parameters needed by as much as 12 times without losing quality. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Lora » Parameter efficient » Transformer
