Why do large models all adopt the Transformer structure?

Large language models predominantly utilize the Transformer structure because it efficiently overcomes critical limitations of previous architectures. Its core innovation, self-attention, directly addresses the challenge of understanding long-range dependencies within sequences, a key requirement for complex language understanding and generation.

This architecture excels due to its superior ability to model dependencies across vast distances in input text. Critically, it enables massive parallelization during training, drastically speeding up model development on modern hardware compared to sequential predecessors like RNNs. Its scalability allows parameters and model depth to be increased substantially to capture intricate linguistic patterns. The uniform processing blocks provide a stable and flexible foundation for large-scale pre-training and subsequent fine-tuning across diverse tasks.

The Transformer's effectiveness underpins revolutionary models powering state-of-the-art results in natural language processing, computer vision, and multimodal systems. Its scalability, parallelizable design, and powerful context processing enable unprecedented model sizes and capabilities, driving breakthroughs in areas like machine translation, question answering, and content creation, fundamentally reshaping the AI landscape.