Transformers remain one of the most important architectures in artificial intelligence thanks to their ability to work with sequential data and analyze long contexts. This architecture, first introduced in the paper Attention is All You Need, is built around the idea of an attention mechanism, which lets a model focus on different parts of the input, spreading its attention across the whole sequence.
Technical features
Attention Mechanism
The defining feature of transformers is the attention mechanism, which lets a model "focus" on different parts of the input regardless of their position in the sequence.
Unlike recurrent neural networks (RNNs), which process data sequentially, transformers can analyze the whole sequence at once, which speeds up training considerably and improves the quality of predictions.
Scalability
Because they process data in parallel, transformers can handle large volumes of information at high speed and with high accuracy. This is especially useful for tasks such as text translation, summarization, processing large text datasets and so on.
Models built on transformers can have billions of parameters, which lets them solve complex tasks such as writing texts, programming and other kinds of content generation.
Input (context) and output window
The input window (or context window) is the maximum number of tokens a model can take in at once for processing. The larger the input window, the more information the model can "remember" within a single request, whether that is text, code or the context of a conversation. GPT-4o, for example, has an input window of 128,000 tokens, so it can take entire books or long documents into account.
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The output window** is the number of tokens a model can generate in reply. For most models this number is limited. In the case of GPT-4o the limit on tokens available for generation is 4096 tokens.
Transformers, especially modern ones such as GPT-4o and LLAMA, have large context windows that let models take long sequences of text into account in a single pass, which makes them useful for writing books and long articles as well as for analyzing large documents and program code.
Examples of use
ChatGPT
This is one of the most popular transformer-based models. ChatGPT is used for text generation, writing articles, holding conversations, programming and translation. Thanks to its large context window, the model can analyze and process large volumes of text in one pass, which makes it especially useful for working with long documents or complex requests.
LLAMA
LLAMA is one of the most advanced transformer-based models. It was designed to use computing resources more efficiently than earlier transformer versions. The model is successfully applied to programming tasks, code generation, article writing and other natural language work.
Claude
Claude is a model developed by Anthropic and designed for safe text generation with an emphasis on multitasking. It is used in complex scenarios such as analyzing large volumes of data, generating technical documentation and improving content. In 2024 Claude 3.5 continued to advance in protecting the confidentiality of user data during interactions.
Gemini
Gemini, developed by Google DeepMind, is used for comprehensive data analysis, text generation and decision automation in healthcare and science. Its applications range from producing medical reports to exploring data in research projects.