Summary of Hut: a More Computation Efficient Fine-tuning Method with Hadamard Updated Transformation, by Geyuan Zhang et al.
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation
by Geyuan Zhang, Xiaofei Zhou, Chuheng Chen
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel approach to fine-tuning pre-trained language models for downstream tasks, addressing the limitations of existing Parameter Efficient Fine-Tuning (PEFT) methods. The direct Updated Transformation (UT) paradigm constructs a transformation directly from the original to the updated parameters, preserving the correlation between them and leveraging semantic features learned during pre-training. Building on this, the Hadamard Updated Transformation (HUT) method efficiently updates the original weight matrix using the Hadamard transformation with two low-rank matrices, offering a more expressive and flexible update mechanism that captures richer parameter features while reducing computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to fine-tune language models that is efficient and effective. Instead of updating all the model’s parameters, it only updates some of them, which makes it faster and easier to use. The new method, called Hadamard Updated Transformation (HUT), uses special transformations to update the model’s weights in a way that keeps the original meaning intact. This allows HUT to perform as well or better than other methods while using fewer computations. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
