Encoder-Decoder Architecture › When to Use Encoder-Decoder Architecture

Michael BrenndoerferUpdated June 20, 20253 min read
Language AI HandbookAll chapters
Part 16 of 67Transformer Architectures
Chapter 113Encoder-Decoder Architecture › When to Use Encoder-Decoder Architecture
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The choice between encoder-only, decoder-only, and encoder-decoder architectures depends on the task structure. Here's a framework for deciding:

Use Encoder-Decoder When:

  • Input and output are both sequences but structurally different: Translation, summarization, style transfer
  • The input requires deep understanding before generation begins: Question answering, data-to-text
  • Input-output alignment is complex and non-monotonic: The output may reorder, add, or drop information from the input
  • The input is much longer than the output: Summarization, where encoding the input once and referencing it repeatedly is more efficient than including it in the decoder context

Use Decoder-Only When:

  • Generation is the primary goal: Creative writing, code generation, conversational AI
  • The task is primarily about extending or continuing: Completion, elaboration
  • Prompt engineering is the primary interface: The input is a short prompt, not a structured document to be transformed
  • Simplicity and scale are priorities: Decoder-only models are architecturally simpler and have dominated recent scaling efforts

Use Encoder-Only When:

  • No generation is required: Classification, NER, extractive QA
  • The task is fundamentally about understanding: Semantic similarity, sentiment analysis
  • Efficiency at inference is critical: Encoders require one forward pass, not autoregressive generation
Out[19]:
Visualization
Comparison chart showing which architecture suits different task types.
Task-architecture alignment. Each architecture excels at different task types. Encoder-decoder models occupy the middle ground, handling tasks that require both deep input understanding and complex output generation.