Summary of Star Attention: Efficient Llm Inference Over Long Sequences, by Shantanu Acharya et al.
Star Attention: Efficient LLM Inference over Long Sequences
by Shantanu Acharya, Fei Jia, Boris Ginsburg
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes Star Attention, a novel approach for improving the computational efficiency of large language models (LLMs) when processing long sequences. The main challenge is the quadratic complexity of self-attention mechanisms, which slows down inference and increases costs. To address this issue, the authors introduce a two-phase block-sparse approximation that shards attention across multiple hosts while minimizing communication overhead. This approach is designed to integrate seamlessly with most Transformer-based LLMs trained with global attention. The results show a significant reduction in memory requirements and inference time by up to 11x while preserving 95-100% of accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make language models work faster on long texts. Right now, it takes a lot of time and energy because the model has to look at all the words in the text. The authors came up with a new way to do this that’s much faster and uses less memory. They call it Star Attention, and it works by breaking down the task into two parts. First, they look at small groups of words together, and then they look at the entire text again. This makes their language model 11 times faster while still being just as good. |
Keywords
» Artificial intelligence » Attention » Inference » Language model » Self attention » Transformer
