Decoder Architecture › When to Use Decoder-Only Models
Michael BrenndoerferUpdated June 19, 20251 min read
Language AI HandbookAll chapters Part 16 of 67Transformer Architectures
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Decoder-only architectures excel at:
- Text generation: Stories, code, conversations, completions
- Language modeling: Predicting the next token given context
- In-context learning: Few-shot learning through prompting
- General-purpose AI assistants: Handling diverse tasks through natural language
They are less naturally suited for:
- Bidirectional understanding: Tasks like named entity recognition or question answering over a document, where seeing the full context helps
- Sequence-to-sequence with misaligned lengths: Machine translation where the source and target have different structures
- Fixed-length encoding: Producing a single vector representation of a document
For these tasks, encoder-only (like BERT) or encoder-decoder models (like T5) may be more appropriate. However, large decoder-only models trained with instruction tuning have proven surprisingly capable across task types, blurring these traditional boundaries.