Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM

The paper titled 'Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning' investigates the intricate relationship between large language models' (LLMs) ability to recognize repetitive input patterns and their performance in in-context learning (ICL). Unlike previous studies that primarily focused on attention heads, this research emphasizes skill neurons, particularly repetition neurons. The findings indicate that the impact of these neurons on ICL performance is contingent upon the depth of the layer in which they are located. By comparing the effects of repetition neurons and induction heads, the authors identify effective strategies for reducing repetitive outputs while maintaining robust ICL capabilities. This work is significant as it not only enhances our understanding of LLMs but also provides practical insights that could lead to improved performance in real-world applications.

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