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arXiv · 2407.07011

Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning

Joy Crosbie·Ekaterina Shutova
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Abstract

Large language models (LLMs) have shown a remarkable ability to learn and perform complex tasks through in-context learning (ICL). However, a comprehensive understanding of its internal mechanisms is still lacking. This paper explores the role of induction heads in a few-shot ICL setting. We analyse two state-of-the-art models, Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks. Our results show that even a minimal ablation of induction heads leads to ICL performance decreases of up to ~32% for abstract pattern recognition tasks, bringing the performance close to random. For NLP tasks, this ablation substantially decreases the model's ability to benefit from examples, bringing few-shot ICL performance close to that of zero-shot prompts. We further use attention knockout to disable specific induction patterns, and present fine-grained evidence for the role that the induction mechanism plays in ICL.

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Joy Crosbie, Ekaterina Shutova. 2024-07-09. Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning. https://arxiv.org/abs/2407.07011

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