Summary of Rethinking Token Reduction For State Space Models, by Zheng Zhan et al.
Rethinking Token Reduction for State Space Models
by Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang
First submitted to arxiv on: 16 Oct 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 A recent surge in State Space Models (SSMs) has led to the development of efficient architectures like Mamba, which can handle long-range dependencies and scale to billions of parameters. To further improve the applicability of Mamba, researchers investigated its efficiency and found that applying existing token reduction techniques directly to SSMs resulted in significant performance drops. By analyzing the limitations of these methods, they proposed a novel, unified post-training token reduction approach for SSMs, integrating token importance and similarity. This method reduces intra-layer tokens using a fine-grained strategy, achieving average accuracy improvements of 5.7% to 13.1% on six benchmarks with Mamba-2 while significantly reducing computational demands and memory requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a special type of State Space Model that’s good at handling long-range dependencies. Scientists wanted to make it even better by making it more efficient. They tried using some existing techniques, but they didn’t work very well. So, they looked into why this was happening and found some problems with those techniques. Then, they came up with a new way of reducing the number of tokens in Mamba that works really well. It’s called a fine-grained intra-layer token reduction strategy. They tested it on six different datasets and found that it improved the accuracy by 5-13% compared to the old methods, while also using less computer power and memory. |
Keywords
» Artificial intelligence » Token
