Why are large language models all based on Transformers?

Large language models primarily leverage the Transformer architecture because it efficiently overcomes critical limitations of prior models like RNNs and LSTMs, enabling superior performance at scale. Its design facilitates parallelization and effective long-range dependency modeling.

The Transformer's core innovation is the self-attention mechanism. This allows every word in a sequence to directly relate to every other word, overcoming RNNs' sequential bottleneck and capturing complex contextual relationships regardless of distance. Furthermore, the architecture's non-recurrent nature enables massive parallelization during training, drastically speeding up learning on modern hardware. Its scalability allows stable learning across billions of parameters. Finally, the encoder-decoder structure provides inherent flexibility, readily adapting to various text generation, translation, and comprehension tasks.

The Transformer's capabilities directly enable the creation of practical, powerful large language models. Its efficiency makes training vast models feasible, while its ability to understand intricate context delivers human-like text generation and advanced reasoning. This foundational architecture drives state-of-the-art performance across diverse natural language processing applications and industries.