Summary of Taipan: Efficient and Expressive State Space Language Models with Selective Attention, by Chien Van Nguyen et al.
Taipan: Efficient and Expressive State Space Language Models with Selective Attention
by Chien Van Nguyen, Huy Huu Nguyen, Thang M. Pham, Ruiyi Zhang, Hanieh Deilamsalehy, Puneet Mathur, Ryan A. Rossi, Trung Bui, Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen
First submitted to arxiv on: 24 Oct 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 This paper proposes Taipan, a novel hybrid architecture that combines State Space Models (SSMs) with Selective Attention Layers (SALs) to efficiently handle long-context language modeling. The dominant Transformers struggle with long sequences due to computational complexity and memory costs. SSMs like Mamba offer alternatives but underperform in tasks requiring extensive in-context retrieval. Taipan balances efficiency with Transformer-like performance by identifying tokens requiring long-range interactions, removing less important features, and augmenting representations using attention modules. This approach extends accurate predictions to context lengths of up to 1 million tokens while preserving computational efficiency. The paper demonstrates Taipan’s superior performance across various scales and tasks, offering a promising solution for efficient long-context language modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making computers better at understanding very long pieces of text. Right now, the best way to do this is with something called Transformers, but they get stuck when trying to handle really long texts because it takes too much computer power and memory. The team has come up with a new idea that combines two other approaches: State Space Models (SSMs) and Selective Attention Layers (SALs). This new approach, called Taipan, is like a filter that helps the computer focus on the most important parts of the text. It can handle texts as long as 1 million words while still being efficient. The team tested Taipan and found it works much better than other approaches for long-text understanding. |
Keywords
» Artificial intelligence » Attention » Transformer
