Simplified explanation of Transformer Attention Block with #TransformerAttentionBlockExplainedSimply

Natural language context can be viewed as a time series where tokens appear in a specific order and can relate to each other even if they are far apart. For example, in a text from the movie “Mullholland Drive,” the word ‘she’ refers back to ‘the woman’ and ‘leaves’ is understood in the context of ‘apartment.’ Convolutional Neural Networks (CNNs) are not ideal for understanding distant relationships in text due to their limited receptive field. The attention mechanism in transformers addresses this issue by modifying the vector representation of each token based on its connection with all other tokens, capturing contextual insights that may not be explicitly articulated.

The attention mechanism computes the connection between each token and all others, adjusting each token’s representation to reflect its relationship with the rest. This process allows tokens to encode insights about the context, which can be utilized in various ways, such as answering questions by identifying the next relevant token. By computing attention between tokens and learning from examples, the model can understand the strength of connections and modify token representations accordingly. This process is akin to the intermediate output of filters in a CNN operating on an image.

Overall, the attention mechanism in transformers enables tokens to encode contextual information by considering their relationships with other tokens, leading to a more nuanced understanding of text data. This mechanism is crucial for tasks like question answering, where tokens need to be processed in a way that captures the context and produces meaningful responses.

Source link

Source link: https://urialmog.medium.com/transformer-attention-block-explained-simply-4c4fca7f2200?source=rss——llm-5