Induction Heads are a specialized two-head attention circuit mechanism in Transformer neural networks that implement in-context pattern copying. A Previous Token Head attends to prior occurrences of a token, while an Induction Head looks back to locate tokens that succeeded it in previous contexts, allowing models to replicate multi-step patterns and complete zero-shot copying.
Helps AI builders design and scale robust architectures; mastering the implementation of Induction Heads improves latency, accuracy, and operational efficiency for mechanistic interpretability, in-context learning analysis, and model capability diagnostics.
Induction Heads are specialized attention circuits in Transformer architectures that execute in-context pattern matching and copying. Discovered by Anthropic researchers, an Induction Head operates in tandem with a Previous Token Head: the Previous Token Head shifts attention to the token immediately following a target, while the Induction Head attends back to prior instances of the target token to predict what comes next. The formation of these heads during early pre-training epochs marks a critical phase change, driving the emergence of zero-shot in-context learning.
Induction Heads are two-head attention circuits that search context history for prior occurrences of the current token and copy the token that followed it, enabling models to perform pattern repetition.
Research by Anthropic showed that the emergence of Induction Heads during pre-training directly coincides with a dramatic phase change in a model's ability to perform in-context learning.
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