Transformer
A transformer is a neural network architecture introduced in 2017 that uses self-attention mechanisms to process sequences of data in parallel, forming the foundation of all modern large language models.
Understanding Transformer
Before transformers, sequence processing relied on recurrent neural networks (RNNs) that processed text one token at a time. Transformers changed everything by introducing the self-attention mechanism, which allows the model to weigh the relevance of every token in a sequence to every other token simultaneously. This parallel processing capability made it possible to train on much larger datasets and capture long-range dependencies in text. The original transformer paper, 'Attention Is All You Need' (Vaswani et al., 2017), introduced the encoder-decoder architecture. Modern LLMs like GPT use only the decoder, while models like BERT use only the encoder. The decoder-only architecture has proven especially powerful for text generation tasks. Self-attention allows transformers to understand contextual relationships. The word 'bank' in 'river bank' versus 'bank account' gets different contextual representations based on surrounding tokens. This contextual understanding is what makes LLMs dramatically better at language tasks than previous architectures. Transformers are now used beyond text: vision transformers process images, audio transformers process speech, and multimodal transformers process multiple data types simultaneously. The architecture has become the dominant paradigm in deep learning across virtually every modality.
How GAIA Uses Transformer
Every LLM that powers GAIA's reasoning layer is built on the transformer architecture. When GAIA reads your emails, plans workflows, or drafts replies, the transformer's attention mechanisms allow the model to understand context across long documents and conversations. This architectural foundation is what enables GAIA to maintain coherent understanding across complex multi-step tasks.
Related Concepts
Large Language Model (LLM)
A Large Language Model (LLM) is a deep learning model trained on massive text datasets that can understand, generate, and reason about human language across a wide range of tasks.
Large Language Model (LLM)
A Large Language Model (LLM) is an artificial intelligence model trained on vast amounts of text data that can understand, generate, and reason about human language with remarkable fluency.
Neural Network
A neural network is a computational model inspired by biological neural systems, consisting of interconnected layers of nodes that learn to transform input data into outputs by adjusting connection weights during training.
Embeddings
Embeddings are dense numerical vector representations of data, such as text, images, or audio, that capture semantic meaning and relationships in a high-dimensional space.
Context Window
The context window is the maximum number of tokens a language model can process in a single inference call, encompassing the system prompt, conversation history, retrieved documents, and generated output.
