This is the final part of my series reading “Attention is all you need”, the foundational paper that invented the Transformer model, used in large language models (LLMs). In the first part, we covered some background, and in the second part we reviewed the architecture of the Transformer model. In this part, we’ll discuss the authors’ arguments in favor of Transformer models.

Why Transformer models?

The authors argue in favor of Transformers in section 4 by comparing them to previously extant options, namely recurrent neural networks (RNNs) and convolutional neural networks (CNNs).

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This article is a continuation of my series reading “Attention is all you need”, the foundational paper that invented the Transformer model, which is used in large language models (LLMs).

In the first part, I covered general background. This part will discuss Transformer model architecture, basically section 3 of the paper. I aim to make this understandable to non-technical audiences, but this is easily the most difficult section. Feel free to ask for clarifications, and see the TL;DRs for the essential facts.

The encoder and decoder architecture

The first figure of the paper shows the architecture of their Transformer model:

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Large Language Models (LLMs) are a hot topic today, but few people know even the basics of how they work. I work in data science, but I also didn’t really know how they work. In this series, I’d like to go through the foundational paper that defined the Transformer model on which LLMs are based.

“Attention is all you need” by Ashish Vaswani et al. from the Proceedings of the 31st International Conference on Neural Information Processing Systems, December 2017. https://dl.acm.org/doi/10.5555/3295222.3295349 (publicly accessible)

This series aims to be understandable to a non-technical audience, but will discuss at least some of the technical details. If the technical parts are too difficult, please ask for clarification in the comments. You’re also welcome to just read the TL;DR parts, which should contain the essential points.

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