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Visual explainer

BERT vs GPT

Two fundamentally different ways to read text: understanding the whole context vs generating the next word.

Two tasks require two different ways of reading text.
Two tasks require two different ways of reading text.

Generating text and understanding text are different problems. A generation model has to guess what comes next based only on the past. An understanding model has the luxury of seeing the entire sentence before deciding what any single word means.

Autoregressive Decoder

GPT uses a causal mask so each word only sees the past.
GPT uses a causal mask so each word only sees the past.

Models in the GPT lineage are decoders. They use causal masking to hide future tokens during training. This forces the model to build a representation for the current token using only the tokens that came before it, learning to predict the next word.

Bidirectional Encoder

BERT removes the causal mask so every word sees the whole sentence.
BERT removes the causal mask so every word sees the whole sentence.

Models in the BERT lineage are encoders. They have no causal mask, allowing every position to attend freely to every other position. To train without generating text, they use masked language modeling: hiding a word in the middle and forcing the model to guess it from both sides.

The Shape of Attention

The attention matrix shows the structural difference.
The attention matrix shows the structural difference.

The difference between the two architectures lives entirely in the attention matrix. A decoder's attention is strictly lower-triangular, cutting off access to the future. An encoder's attention is a full square, drawing context from everywhere.

Where It Breaks

Encoders cannot generate text autoregressively.
Encoders cannot generate text autoregressively.

Because an encoder has no causal structure, it has no concept of "the next token". It is built to refine a complete input. Asking an encoder to write a paragraph doesn't just work poorly—it is mathematically the wrong shape for the task.

The Quick Version

  • Generation needs a strict left-to-right mask; understanding does not.
  • GPT is a decoder-only model trained to predict the next word.
  • BERT is an encoder-only model trained to fill in masked blanks.
  • The difference is a lower-triangular vs full attention matrix.
  • Encoders extract features and labels; decoders generate text.

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