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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe difference between encoder-only, decoder-only, and encoder-decoder Transformers is mainly how their attention blocks are arranged and which tokens each position may use—not three different attention equations. Encoders commonly read a full input bidirectionally; causal decoders limit each position to the prefix; encoder-decoder models combine both patterns and add cross-attention from output positions to encoded input.
The shared attention equation
All three designs build on scaled dot-product attention:
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Attention(Q, K, V) = softmax(QKᵀ / √dₖ)V
Q contains queries, K keys, and V values. The product QKᵀ scores how strongly each query matches each key. Dividing by the square root of the key dimension, √dₖ, controls the score scale before softmax. Softmax turns each row of scores into weights, and multiplying those weights by V produces a weighted sum of value vectors. The original Transformer paper describes the mechanism in Attention Is All You Need.
Self-attention and cross-attention
In self-attention, queries, keys, and values are learned projections of the same sequence representation. In cross-attention, queries come from decoder states, while keys and values come from encoder states. That difference in the source of the vectors is what lets a decoder consult a separately encoded input.
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What a mask changes
A mask is applied to attention scores before softmax. Connections a position cannot use receive a prohibitive score—conventionally negative infinity—and therefore zero weight after softmax. The equation stays the same; the mask changes which query-key connections are available.
Why use multiple heads?
Multi-head attention uses multiple learned query, key, and value projections. Each head computes attention separately; the outputs are concatenated and projected. Heads can learn different relationships among positions, but that does not mean every head has a reliably human-readable linguistic role.
How the three architectures differ
| Architecture | Typical attention pattern | What a position can use | Common task pattern | Examples |
|---|---|---|---|---|
| Encoder-only | Bidirectional self-attention | Input tokens on either side | Representing or classifying a complete input | BERT-like encoders |
| Decoder-only | Causal self-attention | Current and earlier tokens; later target tokens are masked | Next-token prediction and autoregressive generation | GPT-like causal language models |
| Encoder-decoder | Bidirectional encoder self-attention, causal decoder self-attention, and decoder cross-attention | The decoder uses earlier target tokens and can attend to encoded source positions | Conditional sequence-to-sequence tasks, such as translation | The original Transformer; T5 and BART are common examples |
These are typical patterns, not rigid rules for every implementation. Hugging Face documents that a causal decoder model can be run with bidirectional attention for a particular use, while noting that changing its attention mode does not make it an encoder architecture. The Attention Interface documentation distinguishes the selected attention mode from the model’s block architecture.
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How each architecture uses attention
Encoder-only: use the whole input as context
An encoder processes the supplied sequence into contextualized representations. With bidirectional attention, a token can use input tokens to its left and right. This suits tasks where the complete input is available and the goal is to understand or represent it, including classification and embeddings. Google’s Transformer course describes these common encoder uses.
Decoder-only: predict from a causal prefix
A causal decoder generates left to right. When predicting a token, its mask blocks later target positions, so it cannot see the answer token in advance. The sequence probability is factorized into next-token probabilities conditioned on the preceding prefix. During generation, the model predicts a token, appends it to the prefix, and repeats the process.
Encoder-decoder: generate from a separate source
The encoder reads the source sequence and produces contextualized states. The decoder applies causal self-attention to the target prefix and cross-attention to the encoder output. At each output position, decoder queries compare with encoder keys and use encoder values, allowing the output to draw on relevant source positions. The generated sequence is conditioned on both the encoded input and the target tokens generated so far. Hugging Face’s encoder-decoder explanation describes this arrangement.
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Which architecture fits the task?
Choose by the task’s information flow rather than assuming one family is universally better.
- Understanding a complete input: An encoder-only pattern lets each position use context on both sides.
- Continuing a prefix or generating text autoregressively: A causal decoder matches the left-to-right prediction process.
- Turning one sequence into another: An encoder-decoder design keeps source representations separate and exposes them to the decoder through cross-attention.
Four questions make the distinction practical:
- What can each position see? Does it need right-side context, or must it obey a causal prefix?
- What is the input-output structure? Is the task to represent a complete input, continue a prefix, or map a source sequence to a target sequence?
- Where does conditioning live? Is input context part of the same causal sequence, or held in a separate encoder representation?
- What are the sequence and implementation constraints? Consider sequence lengths, attention kernels, caching, batch shape, and hardware.
Attention cost: what the quadratic term does and does not tell you
Google’s course gives a simplified self-attention scaling expression of O(N² · S · D), where N is context length, S is the number of self-attention layers, and D is the number of heads per layer. The key implication is that the sequence-length term is quadratic in this account: increasing sequence length can substantially increase attention computation.
This expression is not a universal wall-clock or memory estimate, and it does not by itself establish that one architecture family is faster. Practical costs also depend on model dimensions, implementation, hardware, batch shape, and optimization. Compare actual systems only with those factors controlled.
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What the original Transformer’s translation results show
The original Transformer paper reported 28.4 BLEU on the WMT 2014 English-to-German task. For English-to-French, its abstract reported 41.8 BLEU for a single model after 3.5 days of training on eight GPUs. Google Research’s publication page displays 41.0 for the English-to-French result, a discrepancy between that page and the arXiv abstract; the two figures should not be silently reconciled. These are historical results from the 2017 paper, not a current head-to-head comparison of modern language-model architectures. See the paper abstract and Google Research publication page.
Further reading
Natural Language Processing with Transformers, Revised Edition by Lewis Tunstall, Leandro von Werra, and Thomas Wolf is an intermediate-to-advanced practical guide to NLP and Transformers. Its 408 pages cover attention mechanisms, the encoder-decoder framework, Transformer anatomy, self-attention, and the encoder, decoder, and encoder-decoder branches. See the O’Reilly publisher listing.
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