What Is a Transformer Model?

Transformer models are a type of neural network that provides the foundation for some of the more popular generative AI applications available today, like ChatGPT, Claude, or Gemini. In fact, GPT stands for generative pre-trained transformer. 

Transformer models have been widely hailed for their applications in natural language processing, such as generating any type of text you can describe in a prompt, translating from one language to another, and recognizing and responding to speech. One of the reasons for their impressive performance during natural language processing tasks is that they can identify and implement patterns found in very large amounts of data. However, transformer models are also a shift forward in technology because of their speedy training times, scalability, and versatility in different applications. Beyond natural language processing, transformers are important for computer vision, predicting fraud, and even biomedical research. 

Explore how transformer models work, what you can do with them, and the challenges associated with these models that future improvements to technology may overcome. 

What is a transformer model?

This refers to a type of deep-learning neural network used for understanding sequences and patterns within data. Transformer models represent a significant shift forward in AI technology due to several mechanisms, like attention, self-attention, parallel processing, and positional encoding, that allow the model to understand the context of a large amount of data and predict the correct output in response to a prompt. 

Why are transformer models important?

One of the biggest reasons transformer models are important is their versatility; they can apply to a wide range of tasks, from generating an essay in seconds to understanding genetic patterns found in DNA. Transformer models represent an improvement over other neural network types, like recurrent neural networks, because transformers can process multiple sequences simultaneously. RNNs, meanwhile, can only process one sequence at a time. This means that transformer models take less training time. Transformers also use positional encoding to determine the patterns between items, which eliminates the need for labeled training materials that are time-consuming to produce. 

How do transformer models work?

Transformer models are neural networks, which means that they process information in a way similar to the human brain, passing the data through layers of nodes to understand and manipulate it. Transformers use encoder-decoder architecture, which means the model contains two AI agents performing separate tasks. An encoder’s job is to compress the data down to the barest elements, while a decoder’s job is to rebuild the data using similar data from training materials. In a transformer, the encoder uses positional encoding, which means that it calculates and assigns mathematical representations of words and phrases to understand how the words relate to one another. The decoder then predicts the output by identifying the words or phrases needed to answer the query, utilizing self-attention. This mechanism helps the model understand the most important parts of the query from the less critical ones. 

Positional encoding

Positional encoding is the method the transformer uses to understand the patterns inherent in data. The model doesn’t consider words to be the building blocks of a sentence but rather tokens, which can represent parts of words, single words, or short phrases. The model breaks down text into tokens through tokenization and assigns a mathematical vector to represent each one. These vectors help the model to understand the sequences in data and how they relate to one another. Positional encoding is another layer of mathematical calculations that helps the model retain the positions of the original tokens. 

Self-attention

Because a transformer can process multiple sequences at once, it can determine what within the data is the most important to inform the output and which will have less impact. This allows the transformer model to make more efficient decisions by ignoring data that won’t have an impact on the final output. For example, self-attention allows a model to follow pronoun references and understand how grammatical structures contribute to the overall meaning of a sentence. 

Applications of transformer models

Transformer models are seen as foundational because they represent such a pivotal breakthrough in artificial intelligence research. They changed how people view AI and what is possible using this tech. The sheer versatility of transformer models highlights their impressive capabilities. Many of the tasks you can use a transformer for fall under the category of natural language processing or computer vision. However, transformer models are also adapted to other uses, like training AI and advancing medical research. 

Natural language processing

Transformer models understand the sequence and patterns found in words and sentences. This ability makes them well-adapted to work with people using natural language in a variety of ways. For instance, you might use them for: 

• Language translation

• Text summarization

• Text generation

• Sentiment analysis

• Virtual customer service help desk

• Responding to voice commands

People, from specialized professionals to everyday people, are developing new ways to use the natural language processing abilities of large language models built on transformers (like ChatGPT). For example, you can use the generative text ability to create a mock interview to help you practice answering questions before you apply for a job, help you study for exams, or write the best resume. 

Computer vision

You can use a visual transformer to perform computer vision tasks using transformers. These models work similarly to text-based models but look at the pixels of images instead of words in a sentence. They perform positional encoding and awareness to predict the correct output. A few CV tasks you can use transformers for include: 

• Image recognition 

• Image classification

• Object detection

• Image segmentation

• Image compression

• Anomaly detection

Other applications

You can also use transformers to learn other types of data patterns. For example, you can use a transformer model to: 

Write code: Similar to the way that transformer models can learn natural human language, they can also learn to use programming languages. You can use these models to help check your code, look for potential errors in code, or generate snippets of code you can reuse. 

Perform signal processing: You can use transformers for signal processing, or analyzing a stream of data. For example, you can use a transformer model to understand EEG signals in a brain scan. 

Protein structure prediction: Scientists and researchers can use transformer models to understand the patterns of molecular structure to develop new drugs and other chemical compounds, as well as to understand the structures of the human body. 

Challenges and limitations of transformer models

While transformer models are a leap forward in artificial intelligence, they still have challenges and limitations that AI researchers are working on. For one thing, these models require more computing power than the average PC is capable of producing. This makes transformer models less accessible to the average person or small company. Another challenge is the energy cost of this computational power, which gives transformer models a significant carbon footprint. 

Another challenge of a transformer model is that it can be less transparent about how it makes decisions and the “thought process” behind creating any given output. This makes it challenging to understand how and why problems occur and what can be done to fix them. 

Learn more about transformer models on Coursera.

Transformer models are an example of how artificial intelligence technology has made leaps and bounds over the past few years. If you want to learn more about how to use transformers and large language models, you can take an online course to learn new skills and earn a credential. Consider Generative AI with Large Language Models, a course offered by AWS and Deep Learning.AI on Coursera. It can help you learn how to work with large language models and gain a certificate you can share with potential employers.