 have demonstrated remarkable performance%2C the lack of transparency in their inference logic raises concerns about their trustworthiness. To gain a better understanding of LLMs%2C we conduct a detailed analysis of the operations of attention heads and aim to better understand the in-context learning of LLMs. Specifically%2C we investigate whether attention heads encode two types of relationships between tokens present in natural languages: the syntactic dependency parsed from sentences and the relation within knowledge graphs. We find that certain attention heads exhibit a pattern where%2C when attending to head tokens%2C they recall tail tokens and increase the output logits of those tail tokens. More crucially%2C the formulation of such semantic induction heads has a close correlation with the emergence of the in-context learning ability of language models. The study of semantic attention heads advances our understanding of the intricate operations of attention heads in transformers%2C and further provides new insights into the in-context learning of LLMs.?quality=80&w=1200)
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- Identifying Semantic Induction Heads to Understand In-Context Learning | AI Research Paper Details
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