This paper explores the role of *induction heads* in in-context learning (ICL) for large language models (LLMs). Induction heads are mechanisms that scan the context for previous instances of a token and use a *prefix matching* mechanism to identify and copy these instances, facilitating pattern recognition and repetition. The study focuses on two state-of-the-art models, Llama-3-8B and InternLM2-20B, and evaluates their performance on abstract pattern recognition tasks and NLP tasks. Key findings include: 1. **Ablation Experiments on Abstract Pattern Recognition Tasks**: A minimal ablation of induction heads (1% or 3%) leads to significant performance decreases, with the impact being more pronounced for ICL tasks compared to zero-shot tasks. For example, in the Llama-3-8B model, a 1% ablation results in a 31.6% decrease in performance, while a 3% ablation causes a 36% drop. 2. **Ablation Experiments on NLP Tasks**: Similar trends are observed in NLP tasks. A 1% ablation of induction heads significantly reduces the ICL benefit, and a 3% ablation further degrades performance. In the InternLM2-20B model, blocking the induction pattern in 1% of induction heads results in performance declines comparable to full head ablations. 3. **Attention Knockout Experiments**: These experiments confirm that induction heads rely on a specific attention pattern, where they attend to tokens that have previously followed similar tokens. Blocking this pattern in 1% of induction heads leads to performance declines similar to full head ablations. The study concludes that induction heads are a fundamental mechanism underlying ICL, and their absence or disruption significantly impairs the model's ability to learn from limited examples. The findings provide empirical evidence for the importance of induction heads in enabling effective few-shot learning in LLMs.This paper explores the role of *induction heads* in in-context learning (ICL) for large language models (LLMs). Induction heads are mechanisms that scan the context for previous instances of a token and use a *prefix matching* mechanism to identify and copy these instances, facilitating pattern recognition and repetition. The study focuses on two state-of-the-art models, Llama-3-8B and InternLM2-20B, and evaluates their performance on abstract pattern recognition tasks and NLP tasks. Key findings include: 1. **Ablation Experiments on Abstract Pattern Recognition Tasks**: A minimal ablation of induction heads (1% or 3%) leads to significant performance decreases, with the impact being more pronounced for ICL tasks compared to zero-shot tasks. For example, in the Llama-3-8B model, a 1% ablation results in a 31.6% decrease in performance, while a 3% ablation causes a 36% drop. 2. **Ablation Experiments on NLP Tasks**: Similar trends are observed in NLP tasks. A 1% ablation of induction heads significantly reduces the ICL benefit, and a 3% ablation further degrades performance. In the InternLM2-20B model, blocking the induction pattern in 1% of induction heads results in performance declines comparable to full head ablations. 3. **Attention Knockout Experiments**: These experiments confirm that induction heads rely on a specific attention pattern, where they attend to tokens that have previously followed similar tokens. Blocking this pattern in 1% of induction heads leads to performance declines similar to full head ablations. The study concludes that induction heads are a fundamental mechanism underlying ICL, and their absence or disruption significantly impairs the model's ability to learn from limited examples. The findings provide empirical evidence for the importance of induction heads in enabling effective few-shot learning in LLMs.