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Which Attention Heads Matter for In-Context Learning?
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Large language models (LLMs) exhibit impressive in-context learning (ICL) capability, enabling them to perform new tasks using only a few demonstrations in the prompt. Two different mechanisms have been proposed to explain ICL: induction heads that find and copy relevant tokens, and function vector (FV) heads whose activations compute a latent encoding of the ICL task. To better understand which of the two distinct mechanisms drives ICL, we study and compare induction heads and FV heads in 12 language models. Through detailed ablations, we discover that few-shot ICL performance depends primarily on FV heads, especially in larger models. In addition, we uncover that FV and induction heads are connected: many FV heads start as induction heads during training before transitioning to the FV mechanism. This leads us to speculate that induction facilitates learning the more complex FV mechanism that ultimately drives ICL.
Forward citations
Cited by 4 Pith papers
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Compressed Sensing for Capability Localization in Large Language Models
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A method that uses a handful of 'semantic retrieval heads' instead of all attention heads to decide which key-value cache entries can be dropped, plus layer-wise cache budgeting, reportedly beats prior KV compression ...
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Distinct Computations Emerge From Compositional Curricula in In-Context Learning
When transformer models see easy component examples before a harder combined math problem in one prompt, they solve unseen versions of the combined problem and store intermediate steps internally, unlike models traine...
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