Inthis post, we focus on the Transformer models in more detail. At the end of the post, you will understand why Transformer models are important. You will be familiar with a variety of technologies developed by Transformers. We will discover the network architecture of the Transformer model and self-attention mechanism. Let’s get started.
We recommend our previous post, if you would like to read a comprehensive guide about LLM.
Transformer model
What are transformer models?
Transformer models are a type of deep neural network, which are similar to other neural network types such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). The heart of Transformer models is the self-attention mechanism, which enables the model to see different parts of a sequence simultaneously and determine the importance of each part. To understand the self-attention mechanism more clearly, imagine you are in a noisy room and trying to listen to a specific voice. Your brain can automatically focus on that specific voice while trying to ignore other voices. Self-attention mechanism works in the same way. It pays attention to different parts of a sequence with different importance to make better output predictions. This mechanism enables Transformer models to be trained on larger datasets efficiently [1].
Transformer models were first described in 2017 by Google in a paper focused on language translation [1]. Since then, they have driven a wave of advances in a variety of Machine Learning tasks such as chatbots, computer vision, robotics, and computational biology. Currently, there are several state-of-the-art products including ChatGPT-4 developed by OpenAI [2], Google Bard [3], Anthropic Claude [4], Midjourney developed by Midjourney research lab [5], DALL·E-3 developed by OpenAI [6], RT-2 developed by Google [7], and CogVideo [8].
Why are Transformer models important?
Transformer models have revolutionized NLP in machine learning technology by allowing models to handle such long-range dependencies in text. This is achieved through the self-attention mechanism, which determines the importance of different parts of a sequence, regardless of their distance from one another. Let me clarify with an example. When we read a text, we always focus on one word, while our mind simultaneously attends to the previous words in memory. The self-attention mechanism performs a similar task, allowing the model to understand the context of each word concerning the rest of the sequence.
Transformer models come with several benefits, including the following:
Enable the training of Large Language Models: With Transformer models, long-range sequences are processed in parallel, which significantly decreases both training and inference times.
Enable faster customization: With transformer models, you can use techniques like transfer learning and Retrieval Augmented Generation (RAG) [9]. These techniques allow pre-training of models on large-scale datasets and then fine-tuning them on smaller, task-specific datasets. This means that it is not necessary to train large models from scratch.
Enable the creation of multimodal AI systems: With transformer models, you can create some multimodal AI systems that can process complex and diverse datasets from different domains. They mimic human understanding and generate more accurate results.
What are applications of Transformers?
Transformers have opened up a world of possibilities in various domains such as NLP, computer vision, robotics, and biology. Transformer models can be trained on any sequential data like human language, music composition, programming languages, and more (as shown in Figure 1). Some of the applications of Transformers are as follows:
Natural Language Processing: Transformers enable machines to read, understand, and generate human language more accurately than previous models. They can summarize long text, and generate coherent and contextually relevant text for many topics. They can also understand and respond to voice commands in virtual assistants like Alexa, and Google Assistant.
Machine translation: Transformer models have significantly improved the fluency and accuracy of translations in comparison to previous methods.
Understanding the structure of DNA and protein: Chains of genes in DNA and amino acids in proteins can be considered as a language (sequential data). Therefore, Transformer models can be used to extract the structure of DNA and proteins. AlphaFold2 system [10] developed by DeepMind is a Transformer used to understand the structure of proteins.
Combining NLP and computer vision capabilities: Vision transformers (ViT) have extensive applications in popular computer vision tasks [11], such as image classification, image segmentation, object detection, and action recognition. ViTs are also applied in generative modeling and multi-model tasks, including visual grounding, visual-question answering, and visual reasoning. With Midjourney [5], and DALL·E-3 [6] systems, high-quality images can be created from simple text prompts.
Programming tasks: The GitHub Copilot system [12] provides contextualized assistance throughout the software development lifecycle, from code completions and chat assistance in the IDE to code explanations and answers to docs in GitHub and more. With Copilot elevating their workflow, developers can focus on more value and innovation.
Figure 1. Applications of Transformer models.
How is Transformer architecture?
The Transformer architecture, as illustrated in Figure 2, follows an Encoder-Decoder structure and comprises several components that work together to generate the final output. Let’s have a closer look at these components.
Input and target embeddings: Neural networks can not process strings directly. Therefore, the Embedding block converts the string into a compact space called embedding vectors, which the network can comprehend.
Positional Encoding: This block is responsible for remembering the relative positions of tokens in a sequence.
Encoder and Decoder components: Encoder and Decoder components consist of modules that are stacked several times on top of each other, as described by Nx. The encoder, situated on the left half of the architecture, processes the entire vectors embedding and then learns how to transform the vectors into a compact and continuous representation. On the other hand, the decoder, located on the right half of the architecture, generates an output sequence. To generate the output, it receives the output of the Encoder together with the Decoder output of the previous step.
Figure 2. The Transformer model [1].
Do Transformer models end?
Despite their strength, Transformers have some shortcomings, including high computational cost and memory complexity. This is because their design scales quadratically with sequence length [13]. To address this issue, researchers have developed modified versions of Transformers, such as longformer [14], reformer [15], and bigbird [16]. These models still use the attention mechanism but are better suited to handle long sequences. Researchers are trying to develop simpler Transformers with fewer parameters that can deliver similar performance to the largest models.
Conclusion
Transformer models are a type of deep neural network that can transform sequential data into other forms of data. The unique feature of Transformers is that, this conversion process is carried out simultaneously on the entire input sequence, unlike previous methods. Due to their significant advantages, Transformers have taken the world stormily. They have pushed the limits of what’s possible in AI by creating a new generation of AI research and technology. Today, we can see the applications of Transformers in a variety of domains, including NLP, computer vision, robotics, and biology.
Thank you for reading this post of Saiwa platform and I hope that it has made Transformer models more understandable to those who are new to the topic. In our upcoming posts, we will delve into the details of the Transformer architecture.
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