Not every Transformer is a chatbot. Since the architecture appeared in the 2017 paper "Attention Is All You Need," it has split into three distinct architectural patterns—encoder-only, decoder-only, and encoder-decoder—and each pattern maps cleanly onto a different family of tasks. Encoder-only models such as BERT excel at understanding input, like judging whether a sentence is positive or negative. Decoder-only models such as GPT build output one token at a time and dominate generation. Encoder-decoder models combine both halves for tasks that transform one sequence into another, like translation. The same core ideas also work outside language entirely: Vision Transformers process images as sequences of patches, and the architecture has been applied to protein folding and biological sequence analysis.
Three Pillars of Transformer Architecture
The Transformer rests on two structural pillars: the encoder and the decoder. The encoder reads and processes the input, transforming it into a format the model can work with—absorbing a sentence and reducing it to its essential representation. The decoder takes that understanding (or its own partial output) and produces results sequentially.
What makes this architecture different from its predecessors is self-attention: rather than processing tokens strictly in order, the model weighs relationships between all parts of the input at once. That single mechanism is why the same blueprint can absorb a sentence, an image, or a protein sequence.
The three architectural categories emerge from how designers combine these pillars. A teaching series on Transformers for social scientists by Lorcan McLaren identifies "three main categories of transformer-based model, each suited to different tasks": encoder-only, decoder-only, and encoder-decoder. The choice among them is not stylistic—it determines what the model can fundamentally do.
Encoder-Only Transformers: Understanding Context
Encoder-only models keep just the reading half of the original architecture. Their defining feature is bidirectional context: as McLaren's guide explains, they "can attend to words both before and after a given position." Every word's representation is shaped by the entire input around it, not just what came earlier.
This bidirectionality is exactly why they are "best suited for classification, text understanding, and analysis tasks." A sentiment judgment depends on the whole sentence—a negation at the end can flip the meaning of everything before it—so a model that sees full context in both directions has the right inductive bias. BERT and RoBERTa are the canonical examples of this family.
What encoder-only models do not do is generate. They build a rich contextual understanding of input but produce no new tokens on their own. Think of them as expert readers: hand them text and they can categorize, compare, and analyze it, but ask them to write a paragraph and they have no mechanism for producing one. Their usefulness ends where comprehension ends.
Decoder-Only Transformers: Generating Sequences
Decoder-only models invert the picture: they discard the encoder and keep only the generative half. Their structure is built around predicting what comes next. Each output token is produced one step at a time, and the model attends to its own previous outputs as it continues.
This is the architecture behind the models most readers will have interacted with. GPT, LLaMA, and Falcon are decoder-only, and this family is used for question answering and content generation. The strength of the design lies in sequential output generation: because each token conditions on everything before it, the model can build coherent long-form output step by step.
The trade-off mirrors the encoder-only case in reverse. A decoder-only model builds its understanding of input through the same left-to-right pass it uses to generate—it lacks the encoder's full bidirectional view of a fixed input. For open-ended generation, that is barely a limitation; the model's job is to continue, not to deliver a fixed verdict on a sealed input.
Encoder-Decoder Transformers: Bridging Input and Output
The third pattern keeps both pillars, and that is precisely its advantage. An encoder-decoder Transformer first processes the complete input into a rich contextual representation, then hands that representation to a decoder that generates an output sequence conditioned on it. The original Transformer, built for machine translation, and T5-style models follow this design.
Translation shows why both halves are needed. The model must fully understand a source sentence—the encoder's job—before committing to any output word, and then produce the translation token by token—the decoder's job. Reading and writing are separated into specialized stages, so the input comprehension is comprehensive and the output generation is controlled.
The pattern generalizes to any task that transforms one sequence into a corresponding but different sequence: the input and output need not even share a vocabulary or modality. That flexibility is what later made encoder-decoder thinking adaptable to settings well beyond paired sentences of text.
Transformers Beyond Language: Computer Vision
The clearest demonstration that these patterns are not language-bound is the Vision Transformer, or ViT. The key insight is simple: a Transformer processes sequences, and an image can be turned into one. As the Vision Transformer reference architecture describes, "an input image is divided into patches, each of which is linearly mapped through a patch embedding layer, before entering a standard Transformer encoder."
In other words, ViT "treats an image as a sequence of fixed-size patches and applies self-attention across them," as a GeeksforGeeks architecture overview puts it. These patch embeddings are analogous to tokens in NLP, converting a two-dimensional image into a linear sequence the standard machinery can consume. Self-attention then captures long-range dependencies between distant parts of the image without any convolution operations.
One problem remains: unlike text, patches have no inherent order, so the model needs a signal for where each patch belongs. ViT solves this with learned positional embeddings—vectors of the same dimensionality as the patch embeddings that are summed with them, as James Briggs and Laura Carnevali explain in Pinecone's ViT explainer. The result is an encoder-only-style design that performs image classification, object detection, and segmentation.
Vision is only one frontier. The Transformer architecture has had documented success in biological sequence analysis, protein folding—notably AlphaFold—video understanding, and even evaluating chess positions. Sequences are sequences, whether they are words, patches, amino acids, or moves.
Choosing the Right Transformer Architecture for Your Task
The table below classifies the three architectural patterns and correlates each with its functional strengths and typical use cases, so you can match a task to an architecture at a glance.
| Architecture Type | Key Characteristic | Primary Tasks | Example Models/Applications |
|---|---|---|---|
| Encoder-only | Processes the entire input with bidirectional attention; builds rich contextual understanding but generates no new tokens | Classification, text understanding, and analysis tasks such as sentiment analysis | BERT, RoBERTa |
| Decoder-only | Generates output token by token, attending to its own previous outputs | Generative tasks including question answering and content generation | GPT, LLaMA, Falcon |
| Encoder-decoder | Encodes the full input first, then decodes a conditioned output sequence | Sequence-to-sequence tasks such as machine translation | Original Transformer, T5-style models |
Read the table as a decision rule rather than a catalog. If your task ends in a verdict about fixed input—classify, score, match—encoder-only fits. If it ends in new content, decoder-only fits. If it maps a fixed input to a transformed output of comparable structure, encoder-decoder fits. The row for each pattern exists because its structural characteristic directly enables its task category: bidirectional context enables judgment, sequential prediction enables generation, and staged encode-then-decode enables transformation.
Applying Transformer Principles: A Decision Framework
To effectively apply Transformer architecture, identify whether the task requires input understanding (encoder-only), output generation (decoder-only), or sequence-to-sequence transformation (encoder-decoder), and consider adaptations for non-language domains like computer vision. The essential move in every case is the same one ViT made: express your data as a sequence of embeddings, and the appropriate architectural pattern follows from what the task must do with that sequence.
The measurable indicator of success is the ability to correctly categorize a new AI task and propose the most suitable Transformer architectural pattern for it. Test yourself against the edges of the map: an image classifier is patch-sequence understanding, so encoder-only; a caption generator is decoder-only; a document translator is encoder-decoder. When you can name the pattern and the reason in one sentence, the framework is yours.
Sources
- Demystifying Transformer Architecture in Large Language Models — Truefoundry
- Transformer (deep learning) - Wikipedia — EN
- 4 Transformers – LLMs for Social Scientists — Lorcanmclaren
- Vision transformer - Wikipedia — EN
- Vision Transformer (ViT) Architecture - GeeksforGeeks
- Vision Transformers (ViT) Explained — Pinecone
