Decoder-Only vs Encoder-Decoder Transformers | Generated by AI
Key Differences Between Decoder-Only and Encoder-Decoder Transformers
Transformers are neural network architectures widely used in natural language processing (NLP) and beyond. The original Transformer (introduced in the 2017 paper “Attention is All You Need”) uses an encoder-decoder setup, while modern large language models (LLMs) like GPT often use a decoder-only design. The shift toward decoder-only models has been driven by their simplicity and effectiveness for autoregressive tasks (e.g., text generation). Below, I’ll break down the main differences.
Core Architectural Differences
- Encoder-Decoder:
- Consists of two symmetric stacks: an encoder (processes the entire input sequence in parallel, using self-attention to capture bidirectional context) and a decoder (generates output autoregressively, using self-attention with causal masking plus cross-attention to the encoder’s output).
- Best for sequence-to-sequence (seq2seq) tasks where input and output are distinct (e.g., machine translation: English → French).
- Handles bidirectional context in the input but unidirectional (left-to-right) in the output.
- Decoder-Only:
- Uses only the decoder component, with self-attention modified by causal masking (each token can only attend to previous tokens, preventing “peeking” at future ones).
- Treats the entire sequence (input + output) as a single stream for autoregressive prediction (e.g., next-token prediction in language modeling).
- Ideal for generative tasks like chatbots, story completion, or code generation, where the model predicts one token at a time based on prior context.
Comparison Table
| Aspect | Decoder-Only Transformers | Encoder-Decoder Transformers |
|---|---|---|
| Components | Single stack of decoder layers (self-attention + causal mask). | Dual stacks: encoder (bidirectional self-attention) + decoder (self-attention, causal mask, cross-attention). |
| Attention Types | Only masked self-attention (unidirectional). | Self-attention (bidirectional in encoder), masked self-attention (in decoder), and cross-attention (decoder attends to encoder). |
| Input/Output Handling | Input and output in one sequence; autoregressive generation. | Separate input (encoded) and output (decoded) sequences; allows parallel encoding. |
| Complexity | Simpler: Fewer parameters, easier to scale and train on massive unlabeled data. | More complex: Higher parameter count, requires paired input-output data for training. |
| Training Objective | Typically next-token prediction (causal language modeling). | Often cross-entropy loss on output sequence, using teacher forcing. |
| Strengths | Excels in open-ended generation; scales well (e.g., GPT-3/4). Efficient for long-context modeling. | Better for structured tasks with clear input-output mappings (e.g., summarization, translation). Handles non-autoregressive decoding. |
| Weaknesses | Limited bidirectional context (can’t “see” full input at once); harder for tasks needing explicit input encoding. | More computationally expensive; less flexible for purely generative pretraining. |
| Examples | GPT series, LLaMA, PaLM. | T5, BART, original Transformer for translation. |
Why the Preference for Decoder-Only?
Decoder-only models have gained popularity because they’re easier to pretrain on vast amounts of text data (just predict the next word) and fine-tune for diverse downstream tasks. This “one model to rule them all” approach simplifies development compared to encoder-decoders, which are more specialized. However, encoder-decoders shine in scenarios requiring strong alignment between input and output (e.g., conditional generation).
If you’re implementing this or have a specific use case in mind, let me know for more tailored advice!
References
- Attention is All You Need (Original Transformer Paper)
- Language Models are Few-Shot Learners (GPT-3, Decoder-Only Example)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5, Encoder-Decoder Example)