Attention in AI: How machines understand context

In neural networks, attention refers to a model's ability to focus specifically on the most relevant elements of an input. Instead of treating all tokens in a sequence equally, the model learns to assign weights that determine which information is particularly important for the current calculation.

In a sentence such as “The cat is sitting on the mat,” for example, the model recognizes that the word ‘mat’ is semantically more closely related to “cat” than to other words in the sentence. This weighting makes it possible to capture dependencies between words or tokens across different distances.

This mechanism creates a contextualized representation vector for each word, which reflects not only the isolated meaning of the word, but also its semantic relationship to other tokens within the sequence. In this way, the neural network can understand complex linguistic structures and efficiently integrate contextual information into its calculations.

The attention mechanism is a calculation method used in neural networks that controls how strongly individual input elements are taken into account during processing. It models dependencies between tokens by assigning each element in a sequence an attention value that indicates how relevant it is in the current context.

While classic network architectures, such as feedforward or recurrent networks, process all inputs with equal weighting, the attention mechanism enables dynamic context adaptation: the weighting is learned during the calculation and can change depending on the input and position. This allows the model to recognize which pieces of information within a sequence belong together in terms of content or influence each other.

In practical applications, this mechanism enables systems to specifically highlight and interpret linguistic, visual, or semantic relationships. This makes the attention mechanism a central component of modern transformer models—it forms the basis for machine learning to not only recognize patterns, but also to grasp meaning in context.

The attention mechanism within the Transformer model ensures that each word (or token) takes into account its relationship to all other words in a sequence. This allows the model to establish grammatical, semantic, and logical relationships—for example, between a subject at the beginning of a sentence and a verb at the end. In the Transformer architecture, encoders and decoders are connected by several layers of self-attention or cross-attention and feedforward networks. These layers use matrix operations to calculate attention weights. In other words, they determine which parts of the input the model focuses on.

The process can be divided into five key steps:

Before Attention can take effect, words or tokens are converted into numerical representations. To do this, they are transformed into vectors in a high-dimensional space using embeddings, which map semantic similarities between words. Position embeddings are added so that the model also knows the order of the tokens. This creates a unique, mathematically processable vector for each word that represents both its meaning and its position in the sentence.

Three new vectors are formed from each embedding through linear transformations:

Query (Q) – asks the “question”: What information is relevant to me?

Key (K) – describes all tokens in the sequence: When am I important to others (and myself)?

Value (V) – contains the actual semantic information that is passed on if the token is classified as relevant.

These three vectors form the basis for calculating the attention weights between the tokens.

For each token, the query vector is multiplied by the key vectors of all tokens (including its own key vector). The result of the multiplication indicates how strongly two tokens are semantically or syntactically linked. Mathematically, this is a scalar product: the higher the resulting value, the stronger the relationship between the tokens. For “technical” reasons, they are divided by the root of the vector dimension. This is the “scaled” part of the scaled dot-product attention. A softmax function is then applied, which normalizes the sum of the scores to 1. This results in a probability distribution that indicates how important each token is for the current token.

The image shows the formula for the “scaled dot-product attention” mechanism, which is a central component of the Transformer model:

The calculated attention weights are now applied to the corresponding value vectors. Tokens that are particularly relevant to the current word receive higher weights; less relevant ones contribute less. The weighted vectors are then added together.

This creates a new contextualized representation vector – it does not describe the word in isolation, but contains the important meanings of the other tokens in the sequence.

Example: Information from “sits” and “mat” is incorporated into the vector for ‘cat’ through weighted vector addition. This creates a vector that describes a cat sitting on a mat, so to speak. The model “understands” that cat – sits – on – mat forms a coherent unit of meaning.

This calculation step is performed for each token.

The name self-attention means that the key, query, and value vectors are formed from the same text—this variant is used to “understand” a text. Later, based on the understood text (the user input), another text, the answer, is generated—this is where cross-attention comes into play.

In practice, the calculation is not performed on the entire vector, but rather the vector is first divided into, for example, eight vectors, each with a length of 1/8. There are also eight query, key, and value matrices, meaning there are eight parallel attention heads. This allows the model to better take multiple semantic levels into account simultaneously and accurately map complex relationships in context.

For example, one head can capture grammatical dependencies (e.g., subject-verb relationships),

another head can capture semantic relationships (e.g., cat – animal),

and another head can identify causal structures (because – therefore).

The results of all heads are concatenated (joined together) and processed by another linear layer.

Six or more of these attention blocks are then stacked on top of each other, as this has been shown to produce better results in practice than using a single block.

Der Transformer ist die Architektur, die den Attention-Mechanismus zum Kernprinzip moderner KI gemacht hat. Er bildet die Grundlage für nahezu alle leistungsfähigen Sprachmodelle der Gegenwart, darunter BERT, GPT, T5 oder LaMDA.

Der Transformer besteht aus zwei Hauptkomponenten: einem Encoder (Kodierer), der den Eingabetext analysiert, und einem Decoder (Dekodierer), der daraus eine Ausgabe erzeugt, beispielsweise eine Übersetzung, Zusammenfassung oder Chat-Antwort. Beide Komponenten bestehen aus mehreren identisch aufgebauten Schichten, die Attention-Mechanismen und Feedforward-Netze kombinieren.

The rigid selection of the most probable token often results in boring, repetitive texts. Sometimes the language model even gets stuck in a loop from which it cannot escape. To solve this problem, instead of just considering the most probable token, a selection of the most probable tokens is considered and one of them is “rolled” out. This makes the responses more lively. The technique is called sampling.

Parameters can be used to control the effects, e.g., the number of most probable tokens from which the roll is made. A “temperature” parameter can be used to post-process the probabilities: a high temperature evens out the probabilities of token selection – unusual texts become more probable. So, for a poem, a high temperature is chosen, while for a faithful translation, a low temperature is better.

With the help of these sampling strategies, a natural-sounding text sequence is gradually created. The result appears to the user as a coherent, meaningful text, but is actually based on a sequence of vectors, matrices, and weighted probabilities.

The attention mechanism has permanently changed the landscape of machine learning and forms the foundation of modern deep learning models. Without it, transformer models such as GPT, BERT, T5, and PaLM would be inconceivable. Its importance is based on several key advantages:

Better context understanding and higher model performance: Attention allows the entire context of an input to be considered simultaneously. This enables the model to recognize semantic dependencies even in long sequences and produce consistent results.

Efficient parallelization: Unlike recurrent neural networks (RNNs), which process inputs sequentially, attention allows the transformer to calculate all tokens in a sequence simultaneously. This parallelizability is what makes training high-performance models on modern GPUs and TPUs feasible in the first place.

Improved interpretability: The attention weights are visualizable and provide insight into which tokens were decisive for a decision. This allows neural decisions to be traced and partially explained, which is an important step toward explainable AI (XAI).

Architectural versatility: Attention is not limited to language. It is successfully used in computer vision (e.g., Vision Transformer, ViT), speech recognition, music analysis, and time series modeling. Wherever relationships between elements of a sequence or image need to be modeled, attention delivers superior results.

Machine translation: Services such as DeepL and Google Translate use attention to correctly interpret ambiguous words in context. This allows the model to recognize whether the word “bank” in a given sentence means ‘seat’ or “financial institution.”

Dialog systems and chatbots: Systems such as ChatGPT or AI-supported customer service platforms use attention to understand the relationship between user questions and context. This enables them to generate coherent, contextually appropriate responses.

Image processing (computer vision): Attention mechanisms highlight relevant regions in an image, such as objects, tumor structures, or textures. In Vision Transformers (ViTs), visual relationships are also modeled in this way.

Speech and audio data analysis: In automatic speech recognition, attention helps to focus on temporally relevant passages, while in music processing, patterns are recognized across multiple time levels.

Web and usage analysis: Modern web applications that integrate AI models often use tracking cookies or local storage mechanisms to collect usage data. This data, such as device information or interaction behavior, can be incorporated into training processes and help make models more context-sensitive, efficient, and secure.

The attention mechanism is the foundation on which today's AI systems are built. It ensures that machines not only “see” words, but also understand meaning to an extent that is astonishingly close to human reading. Whether in translation, chat, or text analysis, attention enables the crucial step: from merely recognizing individual characters to understanding context.