Summary of Learning on Transformers Is Provable Low-rank and Sparse: a One-layer Analysis, by Hongkang Li et al.
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis
by Hongkang Li, Meng Wang, Shuai Zhang, Sijia Liu, Pin-Yu Chen
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: Efficient training and inference algorithms, such as low-rank adaption and model pruning, have been applied with success for learning Transformer-based large foundation models. Despite this, the theoretical understanding of why these methods work is lacking due to the non-convex optimization challenges posed by Transformers’ complex architecture. This paper presents a first-of-its-kind theoretical analysis of the properties of low-rank and sparsity in one-layer Transformers after convergence using stochastic gradient descent. By modeling data based on label-relevant and label-irrelevant patterns, we demonstrate that trainable parameter gradients are low-rank, dependent on the number of label-relevant patterns. We also investigate how model pruning affects generalization while improving computation efficiency and find that proper magnitude-based pruning has a minor impact on testing performance. Numerical experiments support our findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper studies why certain training methods work well for learning big language models called Transformers. These methods help train the models quickly and efficiently, but we don’t fully understand why they’re effective. The researchers in this paper try to answer this question by analyzing what happens when these models are trained using a specific algorithm. They find that the way the model is updated during training has certain properties that make it efficient. They also test how much this affects the model’s performance on real-world tasks and conclude that some types of pruning can help without hurting the model’s ability to generalize. |
Keywords
» Artificial intelligence » Generalization » Inference » Optimization » Pruning » Stochastic gradient descent » Transformer
