Reference 9 stops to get here · leads to 1

Bidirectional Attention

Allowing tokens to attend to both past and future context, used in encoder models like BERT.

Your route here

9 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  3. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  4. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  5. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  6. Representation Learning ✓ understood

    Learning useful features or representations of data automatically, rather than hand-crafting them.

  7. Embedding ✓ understood

    A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.

  8. Attention Mechanism ✓ understood

    A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.

  9. Self-Attention ✓ understood

    A mechanism where each token attends to all other tokens in the sequence to understand contextual relationships.

  10. Bidirectional Attention · you are here ✓ understood

Allowing tokens to attend to both past and future context, used in encoder models like BERT.

This concept is essential for understanding large language models and forms a key part of modern AI systems.

  • Attention
  • BERT
  • Encoder

Where it sits

Before this

Self-Attention
Bidirectional Attention

Leads to

Encoder-Only Model

Explore nearby

Language & LLMs Causal Mask An attention mask ensuring tokens can only attend to previous positions, crucial for autoregressive generation. Language & LLMs BERT Bidirectional Encoder Representations from Transformers - a model that understands context by looking at text from both directions. Language & LLMs Encoder-Only Model A transformer with only encoder layers and bidirectional attention, suited for understanding tasks (BERT family). Language & LLMs Masked Language Modeling A pre-training objective where random tokens are masked and the model learns to predict them from context.