A transformer is a neural network architecture introduced in 2017 that uses self-attention mechanisms to process input sequences in parallel, enabling it to capture long-range dependencies in text. It is the foundational architecture behind virtually all modern large language models.
Before transformers, sequence models like RNNs and LSTMs processed text one word at a time, making them slow to train and limited in their ability to capture relationships between distant words. The transformer architecture, introduced in the landmark paper "Attention Is All You Need," revolutionized natural language processing by replacing recurrence with self-attention.
The key innovation of transformers is the self-attention mechanism, which allows every token in a sequence to attend to every other token simultaneously. This means the model can directly capture relationships between words regardless of their distance in the text. For example, in "The cat that sat on the mat was happy," self-attention helps the model connect "cat" with "was happy" even though many words separate them.
Transformers consist of stacked layers, each containing multi-head self-attention and feed-forward neural networks. Multi-head attention runs several attention computations in parallel, allowing the model to capture different types of relationships simultaneously, such as syntactic structure, semantic meaning, and coreference. Layer normalization and residual connections help with training stability.
Modern LLMs like GPT, Claude, LLaMA, and Gemini are all based on the transformer architecture, typically using the decoder-only variant for text generation. The architecture's ability to be parallelized during training has enabled scaling to billions of parameters, which has been the driving force behind the remarkable capabilities of today's language models.
Input tokens are converted to dense vector representations (embeddings) and combined with positional encodings that give the model information about each token's position in the sequence.
For each token, the model computes attention scores against all other tokens by comparing query, key, and value projections, producing a weighted combination that captures contextual relationships across the entire sequence.
The attention output passes through feed-forward neural networks that apply non-linear transformations, enabling the model to learn complex patterns and features from the attention-enriched representations.
Multiple transformer layers are stacked, with each layer refining the representations. The final layer's output is projected to vocabulary-sized logits for next-token prediction or other downstream tasks.
GPT-4 and similar models use a decoder-only transformer architecture with hundreds of billions of parameters. Each generated token attends to all previous tokens through causal self-attention, enabling coherent long-form text generation.
The original transformer was designed for translation, using an encoder-decoder architecture where the encoder processes the source language sentence and the decoder generates the translation, with cross-attention connecting the two.
Code-focused models like Codex use transformers to understand programming language syntax and semantics, leveraging self-attention to track variable references, function definitions, and control flow across entire files.
The transformer architecture is the foundation of the current AI revolution. Its ability to efficiently process sequences in parallel, scale to enormous sizes, and capture complex patterns in data has enabled the creation of LLMs that can understand and generate human-quality text across virtually every domain.
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