Easy Attention Question 19 of 223

What is causal or masked attention?

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

Causal attention blocks a token from seeing future tokens.

WHY — Tokens instead of words?

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

Attention questions separate people

who only read docs from people who shipped.

Keep it short, concrete,

and tied to GenAI / LLM work.

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
Causal attention blocks a
token from seeing future tokens.
2
That mask is required
for left-to-right language modeling so the model cannot cheat at next-token prediction.
3
Embeddings
GPT-style decoders use it
4
Context mix
BERT-style encoders do not.
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

“That mask is required for left-to-right language modeling so the model cannot ch”

Tokenized output

Thatmaskisrequiredforleft

Token IDs (example)

2987408337471632900

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

Key takeaway

Causal attention blocks a token from seeing future tokens. That mask is required for left-to-right language modeling so the model cannot cheat at next-token prediction.