Transformer models are neural network models that learn context and meaning by tracking relationships in data through a mechanism called self-attention. They are capable of identifying subtle connections allowing them to take input sequences and use them to create relevant outputs. Transformers revolutionized AI by enabling breakthrough performance in natural language processing, computer vision, and generative AI across large language models, translation, and complex reasoning.
In artificial intelligence, accurately understanding and processing human language has always been a significant challenge. Traditional models struggled with capturing complexities and nuances, often falling short in tasks requiring contextual understanding. This need—the demand for more sophisticated language models—grew as applications like real-time translation and intelligent virtual assistants became more integrated into everyday life. But at its core, the problem was one that extended beyond language into other aspects of AI: the difficulty of identifying and understanding the relationships between data points in complex sets.
Transformer models were created to address this issue. Transformer models leverage advanced techniques to understand context and connections within data. Applying detailed mathematical models, they help an AI system organize the chaos of input so that it may comprehend its intended meaning.
Transformer models originated from a groundbreaking 2017 research paper titled "Attention is All You Need," which introduced a new neural network architecture that utilized a mechanism called self-attention to process and understand the context within sequences of data. The concept of attention, which is foundational to Transformers, was itself introduced in 2014 by Dzmitry Bahdanau et al. Bahdanau is a Research Scientist at ServiceNow Research. The name "transformer" was chosen to reflect the model's ability to transform input representations into more meaningful output representations.
The development of the first transformer model marked a significant leap in AI capabilities. The model was trained in less than four days—a significant improvement over the longer and more resource-intensive training times of previous models. Coupled with the model's ability to set new accuracy records in machine translation, this highlighted the potential of transformers.
Transformers led to new advancements in natural language processing (NLP) and laid the foundation for future large language models such as new generative AI (GenAI) solutions. The introduction of transformers has not only enhanced the accuracy and efficiency of language processing; it has paved the way for the creation of more versatile AI applications, cementing its role as an essential element of modern AI.
As transformer models continue to expand to meet the needs of AI researchers and computer scientists, they are also seeing increased specialization. Distinct categories and types of transformers are evolving to meet specific needs. The following are some of the architectures that are found in modern transformers:
Bidirectional encoder representations from transformers (BERT) models are designed to understand the context of words based on their surrounding words in a sentence. BERT processes text bidirectionally, capturing nuances and relationships between words more effectively than previous models. It is commonly used for tasks like question answering and language inference.
Generative pre-trained transformers (GPTs) are autoregressive models that generate text by predicting the next word in a sequence. GPT models, including the popular ChatGPT line, are known for their ability to produce human-like text and are used in many applications, both professional and personal.
Bidirectional and auto-regressive transformers (BART) combine the bidirectional context understanding of BERT with the autoregressive text generation of GPT. It is effective in text generation, summarization, and translation tasks, providing versatile capabilities for processing and creating coherent text outputs.
Multimodal transformers integrate text and image data, making it possible for AI systems to understand and generate content that spans various types of media. These models are foundational for tasks that require simultaneous interpretation of text and visuals, like visual question answering and image captioning.
Vision transformers (ViT) adapt transformer architecture for image processing by treating images as sequences of patches. Each patch is processed similarly to how words are processed in text, allowing the model to capture contextual relationships within the image. ViTs are used in image classification, object detection, and other computer vision tasks.
Transformers are considered deep learning models, which means they fall into the category of neural networks. But that does not mean they are the same as other examples of that technology. Specifically, transformer models differ from recurrent neural networks (RNNs) and convolutional neural networks (CNNs).
Recurrent neural networks address data sequentially, meaning each token is processed one after another, and they may struggle with long-range dependencies because information can get lost over long sequences. Transformers, on the other hand, use self-attention mechanisms that allow them to consider all tokens in the sequence simultaneously. This parallel processing enables transformers to capture long-range dependencies more effectively and train faster than is possible with RNNs.
Convolutional neural networks excel at processing grid-like data (such as images) by detecting local patterns. However, CNNs are less effective at capturing global relationships within the data. Transformers overcome this by using self-attention to weigh the importance of different parts of the input data as part of the greater whole. While CNNs are primarily used for tasks like image recognition, transformers have been adapted for both text and image processing, providing a more versatile set of solutions.
As we alluded to above, transformers were just that for the field of AI—a transformative introduction that addressed key limitations and opened the door for significant innovation. The advantages this technology makes possible are many and varied, but some of the most significant benefits include:
Transformers have a modular architecture, with layers and attention heads that can be scaled up quite readily. This enables the creation of large-scale models that can efficiently handle extensive sequences of data. By processing long sequences in parallel, transformers significantly reduce training and processing times. This efficiency allows for the development of advanced models (like BERT and GPT) which can capture complex language representations across billions of parameters.
Techniques such as transfer learning and retrieval augmented generation (RAG) facilitate faster and more effective customization. Pretrained on large datasets, these models can be fine-tuned on smaller, specific datasets, enabling personalized applications for different industries without the need for extensive investment—in effect, democratizing access to advanced AI.
Transformers support the development of multimodal AI systems that can interpret and generate content from different data types, such as creating images from textual descriptions. By combining natural language processing and computer vision, transformers enable more comprehensive and human-like understanding and creativity.
Transformers drive significant advancements in AI research and industry innovation, such as positional encoding and self-attention mechanisms. Positional encoding helps models track the position of words in a sequence, while self-attention enables them to weigh the importance of different words based on their relevance to the overall context. These innovations have led to the accelerated development of new AI architectures and applications.
Much like the inputs they receive, transformer models are complex and intricate, built on several software layers that operate in concert to create relevant, intelligent outputs. Each of the following components are essential to this process:
- Input embeddings
- Input embeddings convert input sequences into mathematical vectors that AI models can process. Tokens (such as words) are transformed into vectors that carry semantic and syntactic information learned during training.
- Positional encoding
Positional encoding adds unique signals to each token's embedding to indicate its position in the sequence. This ensures the model can preserve the order of tokens and understand their context within the sequence.
- Transformer block
Each transformer block consists of a multi-head self-attention mechanism and a feed-forward neural network. Self-attention weighs the importance of different tokens, while the feed-forward network processes this information.
- Linear/softmax blocks
The linear block maps complex internal representations back to the original input domain. The softmax function then converts the output into a probability distribution, representing the model's confidence in each possible prediction.
Turning complex input sequences into relevant output is no simple task; it relies on several essential steps that incorporate the key components identified above. These software layers attempt to replicate the function of the human brain, operating together to give the system the processing power it needs to solve difficult problems. These neural networks process each part of the data in sequence simultaneously. As they do, the data goes through the following steps:
- The input sequence is transformed into numerical representations called embeddings, which capture the semantic meaning of the tokens.
- Positional encoding adds unique signals to each token's embedding to preserve the order of tokens in the sequence.
- The multi-head attention mechanism processes these embeddings to capture different relationships between tokens.
- Layer normalization and residual connections stabilize and speed up the training process.
- The output from the self-attention layer passes through feedforward neural networks for non-linear transformations.
- Multiple transformer blocks are stacked, each refining the output of the previous layer.
- In tasks like translation, a separate decoder module generates the output sequence.
- The model is trained using supervised learning to minimize the difference between predictions and ground truth.
- During inference, the trained model processes new input sequences to generate predictions or representations.
Transformers have almost limitless applications in business, making it possible to automate complex data processing tasks, enhance customer interactions, and drive innovation in fields like healthcare, finance, and creative industries. Some of the more prominent uses for transformer models include:
- Natural language processing
- Transformers empower machines to understand, interpret, and generate human language more accurately. This supports applications like document summarization and virtual assistants, which rely on a precise grasp language.
- Machine translation
- Real-time, accurate translations between languages are also made possible. Transformers’ ability to handle long-range dependencies and context significantly improves the accuracy of translations—especially compared to earlier find-and-replace solutions.
- Speech recognition
- Speech-to-text applications can be enhanced by accurately transcribing spoken language into written text. This is particularly useful in developing voice-controlled applications and improving accessibility for the hearing impaired.
- Image generation
- Image generation models use transformers to create visual media from textual descriptions, merging natural language processing and computer vision. This capability is used in creative applications, marketing, and more.
- DNA sequence analysis
- By treating DNA sequences similarly to text, transformers can be trained to predict genetic mutations, understand genetic patterns, and identify disease-related regions.
- Protein structure analysis
- Transformers can model the sequential nature of amino acids in proteins, predicting their 3D structures. This understanding is vital for drug discovery and understanding biological processes.
By enabling advanced natural language processing, machine translation, speech recognition, and more, transformers have forever changed how businesses use AI, enhancing operations across industries and markets. That said, not every AI approach makes the best possible use of transformer technology.
ServiceNow stands as an essential partner in properly leveraging AI to optimize business. Built on the AI-enhanced Now Platform®, ServiceNow’s range of applications incorporate AI and transformer models to provide easy access to language understanding, predictive analytics, automated workflows, and more. These tools empower organizations to streamline operations like never before, enhancing their customer interactions, gaining clear insights, and turning complex data into a true competitive advantage.
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