From Copilot with some prompting
Certainly! Let’s discuss the concept of multi-head attention in the context of a Language Learning Model (LLM).
Input Sentence: The sentence “Seagulls fly over the ocean.”
Attention Weight Visualization: The image illustrates how different words in the sentence attend to each other. For instance, if the attention weight between “seagulls” and “ocean” is high, it indicates that these two words are closely related within the sentence.
Multiple Heads: The model employs multiple attention heads (sub-layers) to compute attention from different perspectives. This allows consideration of various contexts and enhances the model’s ability to capture important information.
Multi-head attention is widely used in natural language processing (NLP) tasks, including translation, question answering, and sentiment analysis. It helps improve performance by allowing the model to focus on relevant parts of the input sequence.
Lechuck Park 