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Induction Heads: The Key to Meta-Learning in Language Models
Induction heads are critical structures in language models that enhance their meta-learning abilities by referencing past instances of tokens to predict the next word in a sequence. These heads examine preceding data when a specific token appears, allowing the model to identify the most likely subsequent term based on historical context. Their emergence is marked by a notable 'induction head bump' in training curves, indicating a sudden drop in loss as the model learns to leverage this mechanism. While this phenomenon provides insight into the functioning of language models, it still leaves many questions about their underlying processes unanswered.
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