Transformers: Processing Language in Parallel with Attention
Transformers process all input text at once, weighing which words are most important to each other in parallel. This 'attention' mechanism is the core of models like GPT, allowing them to understand context over long sequences. Text is broken into tokens, turned into vectors, and then contextualized by multiple attention 'heads'. The key footgun is that attention alone is order-agnostic; without explicit positional encodings, the model can't distinguish 'dog bites man' from 'man bites dog'.
Transformers process all input text simultaneously, using an 'attention' mechanism to weigh which words are most important to each other in parallel, much like scanning a whole sentence to grasp its meaning. This architecture is the engine behind modern LLMs. Text is converted into numerical tokens, then mapped to vectors. At each layer, a multi-head attention mechanism lets every token 'look' at every other token, amplifying relevant words and downplaying the rest. This parallel processing is a key advantage over older, sequential models. The main footgun is that self-attention is permutation-invariant—it treats a sentence as a bag of words. To preserve meaning, Transformers must explicitly inject positional information, ensuring the model knows that the order in 'dog bites man' is critical.
Read the original → Wikipedia: Transformer (deep learning architecture)
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- #deep learning
- #llm
- #attention mechanism
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