Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning
This work provides mechanistic insights into ICL, addressing a key gap in understanding how LLMs process patterns, though it is incremental in building on prior research on induction heads.
The paper investigates the role of induction heads in in-context learning (ICL) for large language models, finding that ablating them reduces ICL performance by up to ~32% on abstract pattern recognition tasks and diminishes benefits from examples in NLP tasks.
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.