Summary of Increasing Transformer Token Length with a Maximum Entropy Principle Method, by R. I. Cukier
Increasing transformer token length with a Maximum Entropy Principle Method
by R. I. Cukier
First submitted to arxiv on: 17 Aug 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 three novel methods to extend the sequence length capabilities of transformers while maintaining their efficiency. By introducing an intermediate step between training and inference/generation, these approaches leverage a Maximum Entropy Principle (MEP) to maximize entropy within predefined constraints, utilizing Lagrange Multipliers. The proposed constraint-based methods can linearly scale up the autoregressive character from T to 2T tokens, mitigating the quadratic computational overhead of standard transformers. Although there is added complexity, the authors argue that these methods will still be faster than traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are powerful tools for processing long sequences, but they can be slow when dealing with very long inputs. To solve this problem, researchers have developed three new ways to extend transformer capabilities without sacrificing speed. Each method adds a step between training and using the model, which helps keep computations efficient. The key idea is to use a principle that maximizes randomness while following certain rules. This allows models to process longer sequences in a more linear fashion, making them faster overall. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Transformer
