Summary of Attention with Markov: a Framework For Principled Analysis Of Transformers Via Markov Chains, by Ashok Vardhan Makkuva et al.
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
by Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish, Alliot Nagle, Martin Jaggi, Hyeji Kim, Michael Gastpar
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Theory (cs.IT); Machine Learning (stat.ML)
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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 explores the success of attention-based transformers in various domains, including natural languages. A key factor behind their achievement is the generative pretraining procedure, where models are trained on large text datasets using an auto-regressive approach. To better understand this phenomenon, the authors propose a new framework that combines theory and experiments to study the sequential modeling capabilities of transformers through Markov chains. The framework uses a Markovian source to model data and investigates the interplay between data properties, transformer architecture, learned distributions, and final performance. Theoretical results show the existence of global minima and bad local minima depending on data characteristics and architecture. Experimental findings confirm these results. The paper also discusses implications for higher-order Markov chains and deeper architectures, outlining open problems in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand why attention-based transformers are so good at natural language tasks. It’s like they have a special way of learning from big text datasets. To figure out how they do it, the scientists created a new framework that combines theory and experiments. They used Markov chains to model data and see how different things affect the performance of these transformer models. They found that the architecture and data characteristics can make a big difference in how well the models work. This is important because it means we can improve our models by changing these factors. |
Keywords
* Artificial intelligence * Attention * Pretraining * Transformer
