Transformer Architecture
A Transformer is a neural network design that revolutionized natural language processing (NLP) by introducing a self-attention mechanism. Unlike older architectures (e.g., RNNs or LSTMs), Transformers process entire sequences in parallel, making them highly efficient and more capable of understanding long-range dependencies. Key components include:
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Self-Attention: Allows the model to weigh the relevance of different words (tokens) when generating or interpreting text.
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Multi-Head Attention: Splits the self-attention process into multiple “heads,” enabling the model to focus on different parts of the sequence simultaneously.
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Feed-Forward Layers: Fully connected layers that refine the attention outputs for better feature representation.
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Positional Encoding: Injects positional information about tokens (like word order) since the architecture does not inherently track sequence order.
Because of its parallelizable design and ability to handle context across large spans of text, the Transformer architecture powers many modern Large Language Models (LLMs), achieving state-of-the-art performance in tasks like machine translation, text summarization, and content generation.