It all started here: Attention is all you need

In the ever-evolving landscape of artificial intelligence, one groundbreaking research paper continues to reverberate through the corridors of academia and industry alike: “Attention is All You Need.” The buzz surrounding generative AI has reached a fever pitch, and this seminal paper’s relevance remains undiminished.

Published in 2017 by Vaswani et al., “Attention is All You Need” introduced the world to the Transformer model, a revolutionary neural architecture that fundamentally altered the way we approach natural language processing and generation tasks. In this article, we embark on a comprehensive journey through the key discussions and critical insights offered by this trailblazing research, illuminating why it remains a cornerstone of generative AI in this transformative era.

Key Topics in “Attention Is All You Need” paper

Here are the key topics covered in the “Attention Is All You Need” Transformer research paper in bullet point form:

• Introduces the Transformer, a novel neural network architecture based solely on attention mechanisms.
• Transformers remove recurrence and convolution, which have been the dominant approaches in neural sequence transduction models.
• The Transformer encoder contains stacked self-attention and feedforward layers.
• The Transformer decoder contains stacked self-attention, encoder-decoder attention, and feedforward layers.
• Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions.
• Models positional encodings to make use of the order of the sequence since there is no recurrence.
• Compares various aspects of self-attention to recurrent and convolutional layers for sequence modeling.
• Achieves state-of-the-art results on WMT 2014 English-to-German and English-to-French translation tasks, outperforming prior models.
• Demonstrates the Transformer generalizing well to English constituency parsing in low-resource settings, outperforming prior sequence-to-sequence approaches.
• Provides analysis and ablations of different components of the Transformer architecture.
• Shows attention head visualizations, indicating they learn to perform different tasks and exhibit syntactic/semantic behaviors.

In summary, the paper introduces the Transformer architecture and analyzes its capabilities on translation and parsing tasks compared to recurrent/convolutional approaches.

The Transformer Architecture

The Transformer is a neural network architecture based entirely on attention mechanisms, instead of recurrence (RNNs) or convolution (CNNs).

It has two main components:

1. Encoder: The encoder is a stack of encoder blocks. Each encoder block has two sub-components:
   - Multi-Head Self-Attention layer: This allows the encoder to look at other words in the input sentence as it encodes a specific word.
   - Position-wise Feedforward Neural Network: This just applies a regular fully connected feedforward network to each position separately.
2. Decoder: The decoder also has decoder blocks stacked. In addition to the two sub-components found in the encoder, the decoder inserts a third sub-layer that performs multi-head attention over the output of the encoder stack.

So in summary, the Transformer uses stacked encoder and decoder blocks containing multi-head self-attention and feedforward layers. The self-attention allows it to model dependencies regardless of distance between tokens. This architecture removes recurrence and convolution while achieving state-of-the-art results on tasks like translation.

Transformer Model to Instruction Tuned LLMs

While the transformer models has made its way into several Generative AI applications, in this discussion we are going to focus our “attention” to Instruction Tunes LLMs. So, how did the original Transformer model from “Attention is All You Need” has been adapted to create instruction-tuned LLMs?

Adapting Transformers for Instruction Tuning
The Transformer architecture introduced in “Attention is All You Need” has become the foundation for modern large language models (LLMs) like GPT-3, PaLM, Llama-2 and Claude. However, some modifications were required to adapt Transformers for the instruction tuning training approach used to create useful LLMs.

Instruction tuning involves providing the model with a natural language instruction specifying what task to perform, example demonstrations, and then having the model generate the desired output [1].

To enable a Transformer LLM to follow instructions effectively, the model architecture and training process is adapted in the following ways [2]:

• Text encoding — The input instruction and examples are concatenated into a single text sequence and encoded by the Transformer encoder.
• Decoder-only — Only the Transformer decoder stack is used to generate the output text based on encoded sequence.
• Pretraining — The model is first pretrained on a large amount of text data to learn basic language generation capabilities.
• Instruction tuning — The pretrained model is then trained further on dataset of instructions and demonstrations to perform new tasks.
• Reinforcement learning — The instruction tuning stage can incorporate reinforcement learning to maximize specific reward functions for generation quality.

By leveraging the Transformer’s ability to process long contextual text input and adapting it for instructional inputs, modern LLMs have achieved strong performance on a wide range of NLP tasks in a zero-shot prompting approach. The instruction tuning methodology builds on top of the core Transformer model advances.

Thankyou!

Hope you enjoyed today’s reading and if you did then do consider following. My Medium blog features concise yet insightful articles exploring the latest topics on Artificial Intelligence ( AI ), Large Language Models ( LLM ), Generative AI, and Natural Language Processing ( NLP ). Stay updated by subscribing for a regular dose of cutting-edge knowledge.

References:

• Attention is All You Need, 2017 arXiv:2203.02155.
• Training language models to follow instructions with human feedback, 2022 https://arxiv.org/abs/2203.02155
• PaLM: Scaling language modeling with pathways, 2022 https://arxiv.org/abs/2204.02311