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Understanding ICL: Induction Heads (Natural Language Processing at UT Austin)

Understanding ICL: Induction Heads (Natural Language Processing at UT Austin)

Stanford CS25: V1 I Transformer Circuits, Induction Heads, In-Context Learning

Stanford CS25: V1 I Transformer Circuits, Induction Heads, In-Context Learning

Catherine Olsson - Induction Heads

Catherine Olsson - Induction Heads

A Walkthrough of In-Context Learning and Induction Heads Part 1 of 2 (w/ Charles Frye)

A Walkthrough of In-Context Learning and Induction Heads Part 1 of 2 (w/ Charles Frye)

Attention - General - Copying & Induction heads [rough early thoughts]

Attention - General - Copying & Induction heads [rough early thoughts]

EleutherAI Interpretability Reading Group 220423: In-context learning and induction heads

EleutherAI Interpretability Reading Group 220423: In-context learning and induction heads

Metalearning & Induction Heads [rough early thoughts]

Metalearning & Induction Heads [rough early thoughts]

Few-shot Prompting (Natural Language Processing at UT Austin)

Few-shot Prompting (Natural Language Processing at UT Austin)

Jacob Andreas | What Learning Algorithm is In-Context Learning?

Jacob Andreas | What Learning Algorithm is In-Context Learning?

A Walkthrough of A Mathematical Framework for Transformer Circuits

A Walkthrough of A Mathematical Framework for Transformer Circuits

Towards Monosemanticity: Decomposing Language Models Into Understandable Components

Towards Monosemanticity: Decomposing Language Models Into Understandable Components

Contextual Representation Models | Stanford CS224U Natural Language Understanding | Spring 2021

Contextual Representation Models | Stanford CS224U Natural Language Understanding | Spring 2021