Transformers in Deep Learning: How Self-Attention Changed Modern AI

Before transformers, many sequence models — models designed to work with ordered data such as text — treated language as something to process one step at a time. A recurrent neural network moved through a sentence token by token, carrying information forward as it went. While this structure mimics the way humans process text, it constrains training: long-range relationships are harder to preserve, and each step depends on the step before it.

The transformer changed the approach. Instead of forcing a model to read a sequence in order, the transformer lets each token compare its representation with the rest of the sequence through self-attention. Depending on the layer and model type, those comparisons can bind distant words, apply a causal mask during generation or divide the work across attention heads that learn different relationship types. The transformer architecture handles context more directly and trains far more efficiently on modern hardware.

That design now sits under many of the systems people recognize as modern AI and machine learning, including GPT-style generation, BERT-style language understanding, retrieval-augmented assistants, code generation tools and enterprise AI applications that interpret documents, queries and business context.

A transformer is a deep learning architecture that uses attention mechanisms to model relationships within a sequence. In natural language processing, the sequence is usually a series of tokens: words, word fragments, punctuation marks or other units of text. In other domains, a sequence might represent code, time series values, image patches, audio segments or events.

The core idea is self-attention. For each token, the model calculates how strongly the token should attend to other tokens in the same sequence. Those attention weights shape the representation that moves through the network.

That architectural shift explains why transformers displaced many recurrent neural network (RNN) and long short-term memory (LSTM) models for large-scale language tasks. RNNs process sequences sequentially, which limits parallel training and makes long-range context harder to preserve. Transformers process token relationships in parallel, which aligns well with GPUs and distributed training.

The transformer was originally introduced for machine translation in the 2017 paper Attention Is All You Need by researcher Ashish Vaswani and colleagues. Since then, the same broad architecture has been adapted into encoder-only models such as BERT, decoder-only models such as GPT and Llama, and encoder-decoder models such as T5. Snowflake Arctic also belongs to the modern family of transformer-based LLMs.

A transformer model is built from layers that update token representations. Each layer typically includes attention, a feed-forward network, residual connections and layer normalization. The exact design varies across model families, but the core operations are consistent enough to explain the architecture in a few components.

Self-attention

Self-attention gives each token a way to compare itself with other tokens in the same sequence. The model begins with token embeddings, then projects each embedding into three learned representations: a query, a key and a value:

• The query represents what the token is looking for.
• The key represents what each token offers as a match.
• The value carries the information that will be blended into the next representation.

In scaled dot-product attention, the model compares queries with keys to produce attention scores. A softmax function converts those scores into weights, and those weights determine how much of each value vector contributes to the token’s updated representation.

For example, in the sentence “The analyst opened the report because it contained the latest revenue numbers,” self-attention gives the model a way to connect “it” with “report” rather than treating the pronoun as an isolated token. In longer prompts, the same mechanism helps the model connect an instruction, a document passage, a table description and a question asked several hundred or several thousand tokens later.

Multi-head attention

Multi-head attention runs several attention operations in parallel. Each attention head has its own learned projections, so different heads can focus on different types of relationships.

One head might track syntactic dependencies. Another might focus on named entities. Another might connect a question to a passage that contains the answer. During training, each head learns its own projections, so the division of attention emerges from the examples the model sees rather than from hand-coded rules.

After the heads run, their outputs are concatenated and projected back into the model’s hidden dimension. This gives each token a richer representation than a single attention calculation would provide.

Positional encoding

Self-attention compares tokens directly, but by itself it doesn’t know token order. A transformer needs positional information to distinguish “the model scored the input” from “the input scored the model.”

The original transformer used sinusoidal positional encodings, which added position-dependent values to token embeddings. Many modern models use learned positional embeddings or other positional schemes, including relative position encodings and rotary position embeddings. But the goal is the same across all of them: attach information about order so attention has access to both content and position.

Order carries much of the meaning in language, code, event sequences and time-oriented data. Without positional information, a model could compare tokens across a sequence, but it would lose the structure that distinguishes “the model scored the input” from “the input scored the model.”

Encoder-decoder architecture

In the architecture introduced in Vaswani et al.’s paper, the encoder first turns the input sequence into contextual representations. The decoder then generates the output sequence, using masked self-attention to work from the tokens already produced and cross-attention to refer back to the encoder’s output.

In machine translation, for example, the encoder processed the source sentence, and the decoder produced the translated sentence. Cross-attention gave the decoder a way to refer back to the encoded source text while generating each output token.

Modern transformer variants often use only part of this structure. BERT-style models use the encoder, while GPT-style models use the decoder. T5-style models keep the encoder-decoder design for sequence-to-sequence tasks.

Feed-forward layers, residual connections and layer normalization

Attention is the defining feature of the transformer, but each transformer block also includes feed-forward layers that transform token representations after attention has mixed contextual information across positions.

Residual connections carry information around each sublayer, allowing the model to preserve earlier representations while learning refinements. Layer normalization stabilizes training by normalizing activations inside the network. Together, these components make deep transformer stacks trainable at scale.

The encoder-decoder split is also the easiest way to understand the major transformer variants. Encoder-only, decoder-only and encoder-decoder models all build from the same architectural foundation, but each one trains against a different kind of task.

Encoder-only transformers

Encoder-only models, such as BERT, read an input sequence bidirectionally. Each token can attend to tokens before and after it, which makes the architecture well suited to language understanding tasks.

BERT-style models are commonly used for classification, entity recognition, semantic search, text similarity and other tasks where the model needs to represent an input rather than generate a long output. Because the model sees both left and right context during training, it learns rich representations of sentences, passages and documents.

Decoder-only transformers

Decoder-only models, such as GPT and Llama, generate text one token at a time. They use causal masking, which prevents each position from attending to future tokens. During training, the model learns to predict the next token from the tokens that came before it.

This design dominates current LLMs because it maps directly to generation. A decoder-only model can continue a prompt, answer a question, write code, summarize a document or call a tool by generating the next sequence of tokens. The same causal structure that makes it useful for completion also makes it a natural fit for chat interfaces and agentic workflows.

Encoder-decoder transformers

Encoder-decoder models, such as T5, use one transformer stack to encode the input and another to generate the output. This structure works well for sequence-to-sequence tasks, including translation, summarization and text rewriting.

The encoder gives the model a full representation of the input. The decoder then generates an output while attending back to that representation through cross-attention. For tasks where the input and output have distinct roles, this architecture remains useful.

Efficient transformer designs

Standard self-attention has a computational cost that grows quickly as sequence length increases. Longer context windows require more memory and compute because the model compares many token pairs.

That cost has pushed transformer research toward architectures that keep the attention mechanism while reducing the amount of computation required for long inputs or large models. Sparse attention narrows the set of token-to-token comparisons, linear attention reformulates the calculation to scale more efficiently, and mixture-of-experts (MoE) designs route each token through selected expert networks instead of activating the full model every time. Longformer, for example, uses attention patterns designed for longer documents, while Mixtral helped make MoE-style routing more familiar in open model architectures.

Each design keeps the transformer’s basic attention-centered structure, but changes the computation around it to support longer context windows, lower latency or better cost-performance trade-offs.

Transformers replaced RNNs for many large-scale sequence modeling tasks because they solved several practical problems at once:

Parallelization

With an RNN, sequence order is built into the computation. Token 10 depends on the hidden state from token nine, for example, which depends on token eight, and the chain continues back through the input. That structure gives the model a natural way to process ordered data, but it also limits how much work can happen at the same time. A transformer layer evaluates token relationships across the sequence in parallel, making the architecture far better matched to GPUs and distributed training.

Dependency

The second problem was long-range dependency. RNNs compress earlier information into a hidden state that moves forward through the sequence. LSTMs improved that design with gates that helped preserve information, but very long dependencies still remained difficult. In a transformer, attention creates a more direct route between distant tokens. A token near the end of a document can attend to a token near the beginning without waiting for information to pass through every intermediate step.

Scaling

As researchers trained larger models on larger data sets, transformers showed predictable gains from more data, more parameters and more compute. That scaling behavior helped turn the architecture from a machine translation breakthrough into the foundation for modern LLMs.

Training speed

Training speed was also a challenge. The original paper on transformer architecture framed this as a practical training advantage. By processing many token relationships at once, the architecture trained faster than comparable recurrent or convolutional models on the translation tasks the researchers tested. In practice, that training efficiency helped make large language models economically and technically feasible.

Transformers solved a core modeling problem: how to represent relationships across a sequence without processing every token one step at a time. In enterprise AI, that strength only holds up when the surrounding system can supply the right context, enforce the right permissions and evaluate the output the model returns.

A document assistant reviewing contracts, for example, has to interpret the user’s question, retrieve the right clauses, preserve the surrounding context and generate an answer that stays grounded in the source material. Similar mechanics sit behind text-to-SQL interfaces, support search, code generation, summarization and agentic workflows, even when the user never sees the transformer model directly.

This makes model selection an infrastructure decision as much as a modeling decision. A smaller model might handle classification, extraction or routing at lower cost, while a larger model may be justified for code generation, long-document synthesis or tasks that require more complex reasoning. For retrieval-augmented generation (RAG), accuracy also depends on chunking strategy, metadata filters, access controls and the freshness of the underlying content. The generator is only one part of the path.

Snowflake Cortex AI gives teams access to fully managed LLMs, RAG and text-to-SQL services inside Snowflake, so organizations can build generative AI applications and analyze unstructured data without managing the underlying model infrastructure directly. Snowflake Cortex AI Functions also support unstructured analytics on text and images using LLMs from providers such as OpenAI, Anthropic, Meta and Mistral AI.

For teams that build or bring their own models, Snowflake ML includes the Snowflake Model Registry, which manages models and metadata in Snowflake and supports inference from registered models. Snowflake ML also supports model management and serving workflows, including deployment to Snowpark Container Services for inference.

Transformers changed deep learning by giving models a different way to handle context. That shift explains why transformers moved so quickly from research architecture to production infrastructure. Once context could be represented at scale, larger models became more practical to train, adapt and apply across enterprise workflows.