Summary of Laser: Attention with Exponential Transformation, by Sai Surya Duvvuri et al.
LASER: Attention with Exponential Transformation
by Sai Surya Duvvuri, Inderjit S. Dhillon
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes a new attention mechanism called LASER, which aims to improve the learning efficiency of large-scale language models by addressing poor gradient signals during backpropagation. The authors analyze the gradients of the softmax-based dot-product attention used in Transformers and find that they can be small, leading to inefficient learning. To mitigate this issue, LASER Attention is introduced, which admits a larger gradient signal and can be implemented with minor modifications to existing attention implementations. Experiments on autoregressive large language models (LLMs) demonstrate significant improvements in downstream evaluations, achieving 3.38% and an average of ~1% improvement over standard attention. The proposed mechanism also yields relative improvements in generalization performance across various tasks, including Vision Transformer (ViT), Conformer, and BERT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving a popular AI technique called Transformers. It’s a way to understand and generate text that has been very successful. However, the authors found that this technique can be slow to learn new information. They propose a new method called LASER Attention that helps solve this problem. The new method makes small changes to how attention is calculated in the Transformer model. The authors tested their approach on large language models and found that it improves performance by 1-3%. This means that AI systems can better understand and generate text, images, and speech. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Backpropagation » Bert » Dot product » Generalization » Softmax » Transformer » Vision transformer » Vit
