Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning
<|reference_start|>Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning: 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.<|reference_end|>
@article{crosbie2024induction,
    title={Induction Heads as an Essential Mechanism for Pattern Matching in
  In-context Learning},
    author={Joy Crosbie and Ekaterina Shutova},
    journal={arXiv preprint arXiv:2407.07011},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07011},
    primaryClass={cs.CL}
}