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🤯 Did You Know (click to read)
Some attention heads specialize in syntactic structure while others capture semantic meaning in language models.
Each attention head learns unique patterns, such as syntax, semantics, or positional relationships. Outputs of all heads are concatenated and projected, allowing the model to encode diverse contextual information. This improves generalization and allows nuanced interpretation of language.
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💥 Impact (click to read)
Multi-head attention enhances model capability for tasks like translation, summarization, and question answering.
Understanding multi-head mechanisms helps AI practitioners design interpretable and effective Transformer models.
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