Summary of Increasing Model Capacity For Free: a Simple Strategy For Parameter Efficient Fine-tuning, by Haobo Song et al.
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
by Haobo Song, Hao Zhao, Soumajit Majumder, Tao Lin
First submitted to arxiv on: 1 Jul 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 proposed CapaBoost strategy enhances model capacity by leveraging low-rank updates through parallel weight modules in target layers. By applying static random masks to the shared weight matrix, CapaBoost constructs a diverse set of weight matrices, effectively increasing the rank of incremental weights without adding parameters. This approach can be seamlessly integrated into various existing parameter-efficient fine-tuning methods and demonstrates significant improvements over baselines on diverse downstream tasks, including natural language understanding, question answering, and image classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make large pre-trained models better at doing specific tasks. They found that some parts of the model can be updated without changing everything else, which helps with small datasets or limited computing resources. The method is called CapaBoost and it works by creating many different versions of the same part of the model. This allows the model to learn more about the task without needing a lot of extra data or computing power. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Language understanding » Parameter efficient » Question answering
