Summary of Parallelizing Linear Transformers with the Delta Rule Over Sequence Length, by Songlin Yang et al.
Parallelizing Linear Transformers with the Delta Rule over Sequence Length
by Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, Yoon Kim
First submitted to arxiv on: 10 Jun 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 This paper proposes a hardware-efficient algorithm for training linear transformers with the delta rule, called DeltaNet. The goal is to create a model that can perform in-context retrieval tasks efficiently while still being able to learn complex patterns like transformers with softmax attention. To achieve this, the authors develop a memory-efficient representation for computing products of Householder matrices, allowing them to scale up the training of DeltaNet models to standard language modeling settings. The resulting 1.3B model outperforms recent linear-time baselines in terms of perplexity and zero-shot performance on downstream tasks. Additionally, the paper explores hybrid models that combine DeltaNet layers with sliding-window attention or global attention, which further improves performance compared to strong transformer baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to train machines so they can quickly understand and remember language patterns. The old ways didn’t work well for big tasks, but this new method uses special math tricks to make it faster. The team trained a super-sized model that did really well on lots of different tests. They also tried mixing their new approach with some older ideas, which made the results even better than strong machines from before. |
Keywords
» Artificial intelligence » Attention » Perplexity » Softmax » Transformer » Zero shot
