Summary of Longvq: Long Sequence Modeling with Vector Quantization on Structured Memory, by Zicheng Liu et al.
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory
by Zicheng Liu, Li Wang, Siyuan Li, Zedong Wang, Haitao Lin, Stan Z. Li
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a novel method called LongVQ to address the limitations of transformer models in processing long sequences while maintaining performance. The self-attention mechanism is computationally costly, leading to limitations for practical applications. Existing attention variants improve efficiency but struggle to abstract global information effectively. State-space models are better suited for long sequences but lack local information capture. The proposed LongVQ method uses vector quantization (VQ) to compress global abstraction as a length-fixed codebook, enabling linear-time computation of the attention matrix. This approach maintains dynamic global and local patterns, addressing the limitations of previous methods. Experiments on benchmarks like the Long Range Arena, autoregressive language modeling, and image and speech classification demonstrate significant improvements over other sequence models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem with machine learning models that are good at working with short sequences of data but struggle when dealing with longer ones. The current solution is slow and doesn’t work well for capturing both global patterns and local details. Researchers are trying to combine two different types of models: one that’s good at long sequences, and another that’s good at complex local patterns. However, this combination has some issues. To solve these problems, the scientists propose a new method called LongVQ. It uses an innovative way to compress information from longer distances into smaller, more manageable pieces. This allows the model to work faster and better with longer sequences while still capturing important details. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Classification » Machine learning » Quantization » Self attention » Transformer
