What role does Transformer play in large models?

The Transformer architecture serves as the foundational backbone for modern large language models, enabling efficient processing of sequential data through its self-attention mechanism. It replaced older recurrent neural networks (RNNs) as the dominant paradigm.

Its key innovation is the self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence simultaneously. This enables parallel computation and efficient learning of long-range dependencies, scaling effectively to handle massive datasets and parameters. Transformers are universal across NLP tasks and are increasingly used in other modalities like vision and audio. The architecture's scalability makes it essential for building large models.

Transformers form the core of nearly all state-of-the-art LLMs like GPT series, BERT, and T5. They empower applications such as machine translation, text generation, summarization, question answering, and chatbots. Their ability to capture complex patterns in context drives advancements in conversational AI and multimodal systems, underpinning generative AI tools with broad real-world utility.