🤯 Did You Know (click to read)
Some heads specialize in syntax, others in semantic roles, and together they provide a comprehensive understanding of sentence structure.
In multi-head attention, the model splits the attention mechanism into multiple 'heads', each learning to attend to different parts of the sequence or relationships between tokens. The outputs of all heads are concatenated and linearly transformed, providing richer representation and capturing diverse contextual dependencies. This enables Transformers to understand complex linguistic patterns, syntactic structures, and semantic nuances.
💥 Impact (click to read)
Multi-head attention enhances the model’s ability to process long-range dependencies and subtle relationships in language, improving performance in translation, summarization, and question-answering tasks.
For researchers, multi-head attention provides insight into how Transformers encode multiple perspectives of context, informing interpretability and model design.
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