BERT vs GPT: Bidirectional Understanding vs Autoregressive Generation
Overview BERT and GPT are both transformer-based language models, but they’re built from opposite halves of the transformer and trained for opposite jobs. BERT uses an encoder trained to fill in masked words using context from both directions, making it suited to understanding text, while GPT uses a decoder trained to predict the next word from only what came before, making it suited to generating text. Comparison Diagram BERT GPT Bidirectional Encoder Autoregressive Decoder the cat [MASK] on mat sees full sentence context (left + right) to fill the mask -> classification, embeddings, NER the cat sat on mat ? each token sees only itself + prior tokens (causal mask) -> generation, chat, completion Comparison Table Aspect BERT GPT Architecture Encoder-only transformer stack Decoder-only transformer stack Pretraining objective Masked language modeling: predict randomly hidden tokens, plus next-sentence prediction Causal language modeling: predict the next token given all prior tokens Attention pattern Bidirectional self-attention; every token attends to the full sequence Causal (masked) self-attention; each token attends only to itself and earlier tokens Output generation One contextual embedding per input token, produced in a single forward pass Text generated autoregressively, one token at a time, each output fed back as input Typical adaptation Fine-tuned with a task-specific head on top of the pretrained encoder Adapted via prompting, instruction tuning, or fine-tuning to continue text Primary use cases Classification, named entity recognition, semantic search, sentence embeddings Open-ended generation, chat, code completion, summarization Inference cost per query Fixed: one pass regardless of desired output Scales with number of generated tokens, each requiring a forward pass Key Differences BERT’s encoder attends to both left and right context; GPT’s decoder attends only to prior tokens. BERT trains on masked language modeling; GPT trains on next-token prediction. BERT produces embeddings in a single pass; GPT produces text through autoregressive decoding. BERT is optimized for understanding tasks; GPT is optimized for generation tasks. When to Use Each BERT ...