Autoregressive Model

Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation.

Achieving task performance by providing a small number of examples inside the prompt without weight updates.

Stepwise reasoning patterns that can improve multi-step tasks; often handled implicitly or summarized for safety/priv...

Architecture that retrieves relevant documents (e.g., from a vector DB) and conditions generation on them to reduce h...

An RNN variant using gates to mitigate vanishing gradients and capture longer context.

Injects sequence order into Transformers, since attention alone is permutation-invariant.

Converting text into discrete units (tokens) for modeling; subword tokenizers balance vocabulary size and coverage.

Training objective where the model predicts the next token given previous tokens (causal modeling).