Summary of Sparser Is Faster and Less Is More: Efficient Sparse Attention For Long-range Transformers, by Chao Lou et al.
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers
by Chao Lou, Zixia Jia, Zilong Zheng, Kewei Tu
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces SPARSEK Attention, a novel sparse attention mechanism designed to efficiently accommodate long sequences in autoregressive Transformers. The approach integrates a scoring network and a differentiable top-k mask operator to select a constant number of KV pairs for each query, enabling gradient-based optimization. This leads to linear time complexity and constant memory footprint during generation. Experimental results show that SPARSEK Attention outperforms previous sparse attention methods and provides significant speed improvements during training and inference in language modeling and downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make large language models (LLMs) work better with long sequences of text. It’s called SPARSEK Attention, and it helps LLMs process long sequences more efficiently. This is important because LLMs are used for many applications that require processing long sequences, such as language translation and text summarization. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Inference » Mask » Optimization » Summarization » Translation
