Easy GPT vs BERT Question 24 of 223

Why is GPT generally better than BERT for generating long answers?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: A SENTENCE BECOMES TOKENS

The model does not read letters like humans. It reads these pieces, then predicts the next one.

Simple meaning

GPT is trained as a language model that writes the next token, so generation is its native task.

WHY — GPT vs BERT instead of guessing?

Why interviewers care about GPT vs BERT:

They are checking judgment

on GPT vs BERT.

A good answer names

the situation, the default choice, and one exception - that reads as experience.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

End with when you use it and one common pitfall.

STEPS — What happens with tokens?

Before the model can read a sentence, it goes through these steps:

1
GPT is trained as
a language model that writes the next token, so generation is its native task.
2
BERT is trained to
reconstruct masked tokens, not to continue a story.
3
You can force BERT-like
models to generate, but decoder-only LLMs are the production default for chat.
4
Context mix
Attention looks at nearby tokens together.
5
Next token
The model scores what should come next.
6
Decode
IDs turn back into readable text.

EXAMPLE — See it in action

Let's see how a real sentence is tokenized (tokens may vary by model):

Input text

“BERT is trained to reconstruct masked tokens, not to continue a story.”

Tokenized output

BERTistrainedtoreconstructmasked

Token IDs (example)

2987408337471632900

Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).

Key takeaway

GPT is trained as a language model that writes the next token, so generation is its native task. BERT is trained to reconstruct masked tokens, not to continue a story.