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Self-attention lets positions exchange information within the same sequence; cross-attention lets one sequence retrieve information from another. In the original encoder-decoder Transformer, both encoder and decoder use self-attention, while the decoder also uses cross-attention to consult the encoder’s output. The key difference is where the queries, keys, and values come from—not a wholly different attention calculation.
What “self” and “cross” mean in attention
Attention uses queries (Q), keys (K), and values (V). A query is compared with keys to determine how much each corresponding value contributes to the result. The names self-attention and cross-attention describe which representation supplies those inputs.
Self-attention: one stream attends to itself
In self-attention, Q, K, and V are formed from the same sequence or set of representations. Each position can use information from other positions in that set to build its contextual representation, subject to the model’s attention mask. For example, a token’s representation can incorporate relevant information from other tokens in the same input.
Cross-attention: one stream attends to another
In cross-attention, queries come from one representation set, while keys and values come from a second. The querying stream can therefore retrieve information from the other stream. Both forms use query-key comparisons to weight values; the distinction is the origin of Q versus K and V.
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Self-attention vs. cross-attention at a glance
| Question | Self-attention | Cross-attention |
|---|---|---|
| Where do Q come from? | The sequence being updated | The querying sequence |
| Where do K and V come from? | The same sequence as Q | A different, source sequence |
| Which positions are updated? | Positions in the sequence supplying Q, using information from that sequence | Positions in the querying sequence, using information from the source sequence |
| Typical interaction dimensions | For length n, n × n | For query length n and source length m, n × m |
| Is causal masking part of the definition? | No. A mask depends on the architecture and task. | No. A mask depends on the architecture and task. |
Where attention appears in an encoder-decoder Transformer
The original Transformer uses attention in three places: self-attention in the encoder, masked self-attention in the decoder, and cross-attention in the decoder. The encoder-decoder connection is described in the original paper: “The best performing models also connect the encoder and decoder through an attention mechanism.” (Vaswani et al., Attention Is All You Need (2017).)
Encoder self-attention
Encoder positions exchange information within the input sequence. In the original translation setup, the encoder processes the available source sentence, so this self-attention does not need to hide later source positions behind a causal mask.
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Decoder self-attention
During autoregressive generation, each target position can use earlier target positions, but not future target tokens. A causal mask enforces that restriction. This is still self-attention because Q, K, and V come from the decoder’s target-side representations; the mask does not turn it into cross-attention.
Decoder cross-attention
The decoder’s queries attend to keys and values from the encoder’s output representations. That gives target-side generation access to the encoded source while the decoder produces its output. In this original pattern, the decoder’s cross-attention is distinct from its masked self-attention.
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Does cross-attention use a causal mask?
Cross-attention is not defined by whether it is causal. Its defining feature is that Q comes from one stream and K and V from another. In the standard encoder-decoder translation pattern, the causal restriction applies to decoder self-attention so the model cannot see future target tokens. Whether cross-attention is masked depends on the architecture and task; the core definition alone does not require a causal mask.
How their interaction costs compare
With standard pairwise attention, self-attention over n positions forms an n × n interaction matrix, so the number of position comparisons grows quadratically with sequence length. Cross-attention between n query positions and m source positions forms an n × m matrix. That dimensional difference does not mean cross-attention is automatically faster or cheaper: the result depends on both lengths, implementation, caching, and the rest of the model. A survey of efficient Transformers also cautions that asymptotic complexity alone does not necessarily predict real-world throughput or latency.
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A scoped example: fine-tuning cross-attention
A 2021 machine-translation study examined adapting pretrained Transformers when source or target languages changed. In the translation settings tested, fine-tuning only cross-attention parameters was nearly as effective as fine-tuning all model parameters (Bapna and Firat, “Simple, Scalable Adaptation for Neural Machine Translation”). This is a result for that study’s experiments, not a general rule that cross-attention is always more important or that it is the only part worth adapting.
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Which one is being used?
- If Q, K, and V are derived from one representation stream, it is self-attention.
- If Q comes from one stream and K and V from another, it is cross-attention.
- If a decoder can only use earlier output tokens, that describes causal masking of decoder self-attention—not the definition of cross-attention.
- In the original encoder-decoder Transformer, decoder queries use cross-attention to access encoder outputs.
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