Summary of Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks, by Jerome Sieber et al.
Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks
by Jerome Sieber, Carmen Amo Alonso, Alexandre Didier, Melanie N. Zeilinger, Antonio Orvieto
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 introduces a Dynamical Systems Framework (DSF) that allows for principled investigations of various attention-based architectures, including softmax attention, linear attention, State Space Models (SSMs), and Recurrent Neural Networks (RNNs). These architectures are commonly used in foundation models for AI applications but have limitations due to their quadratic complexity. The DSF facilitates rigorous comparisons between these models, providing insights into their distinctive characteristics and differences. For instance, the framework reveals that linear attention and selective SSMs share similarities under certain conditions. Additionally, the paper demonstrates how the DSF can be used to approximate softmax attention and provides empirical validations and mathematical arguments to support its claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make AI models faster and more efficient. Right now, some of these models are very good at understanding long pieces of text or speech, but they take a long time to process it all. The authors want to find a way to make them work faster without losing their abilities. They do this by looking at different ways that models pay attention to the information they’re processing. Some models use a “softmax” approach, while others use “linear attention” or even simple math formulas like State Space Models (SSMs) and Recurrent Neural Networks (RNNs). The authors create a special framework called the Dynamical Systems Framework (DSF) that lets them compare these different approaches and figure out which one is best for certain tasks. This can help us make AI models that are faster, better, and more useful. |
Keywords
» Artificial intelligence » Attention » Softmax
