Summary of Fundamental Limitations on Subquadratic Alternatives to Transformers, by Josh Alman et al.
Fundamental Limitations on Subquadratic Alternatives to Transformers
by Josh Alman, Hantao Yu
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); 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 In this research paper, the authors investigate the scalability issues of the Transformer architecture in Large Language Models. Specifically, they focus on the attention mechanism, which is a core component of Transformers and has become a performance bottleneck due to its quadratic time complexity. The authors review various approaches proposed by researchers to accelerate attention computations, including heuristic algorithms and alternative mechanisms. One such alternative is state space models like Mamba, which aim to replace attention with an almost linear-time computation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how the Transformer architecture can be improved for faster processing of large language models. The main idea is to make the attention mechanism more efficient so it doesn’t slow down other parts of the model. This has been a challenge because attention needs to look at all the words in the input text and calculate how important each word is. To solve this problem, researchers have suggested new ways to do attention that are faster, such as using state space models like Mamba. |
Keywords
» Artificial intelligence » Attention » Transformer
