Moderate GPT vs BERT Question 84 of 223

How does bidirectional context in BERT differ from unidirectional context in GPT?

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

BERT sees tokens on both sides of a masked position, which helps disambiguation for NLU.

WHY — Tokens instead of words?

LLMs use tokens (not full words) because it helps them:

This is a process

question about GPT vs BERT.

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trade-offs, and what you would actually do on a GenAI / LLM project - not buzzwords.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

STEPS — What happens with tokens?

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

1
BERT sees tokens on
both sides of a masked position, which helps disambiguation for NLU.
2
GPT may only see
the left context when predicting the next token, which matches generation.
3
That training difference, not
just size, drives the typical use cases.
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

“GPT may only see the left context when predicting the next token, which matches ”

Tokenized output

GPTmayonlyseetheleft

Token IDs (example)

2987408337471632900

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

Key takeaway

BERT sees tokens on both sides of a masked position, which helps disambiguation for NLU. GPT may only see the left context when predicting the next token, which matches generation.