What model architecture is foundational to modern LLMs?

The foundational model architecture to modern large language models (LLMs) is the Transformer. Introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, the Transformer architecture revolutionized natural language processing (NLP) by using self-attention mechanisms, allowing it to encode the relationship between words in a sentence effectively. This enables the model to capture context and dependencies without being constrained by the sequential processing nature of earlier models like Recurrent Neural Networks (RNNs).

The architecture comprises an encoder-decoder structure, although many LLMs utilize either just the encoder (like BERT) or just the decoder (like GPT), demonstrating the flexibility of the Transformer architecture across various tasks. The self-attention mechanism allows the model to weigh the importance of different words in a context, providing a more nuanced understanding of language nuances and complexities.

In contrast, Recurrent Neural Networks are more traditional and dependent on processing data sequentially, which can lead to difficulties with long-range dependencies. Generative Adversarial Networks (GANs) are used primarily in generative tasks like image generation, rather than language. Neural Style Transfer focuses on artistic style application to images and is unrelated to the architecture of LLMs. Thus