Large Language Models & Generative AI

Generative AI and Large Language Models (LLMs) produce text, imagery, audio, and synthetic code by modeling high-dimensional data distributions. The modern paradigm shifted with the 2017 Google Research paper 'Attention Is All You Need' by Vaswani et al., which introduced the self-attention-based Transformer architecture, eliminating the recurrence bottleneck of RNNs. Autoregressive decoder-only models (like OpenAI's GPT series) predict subsequent tokens, while masked encoder models (like Google's BERT) capture bidirectional contextual semantics. Generative image models leverage Generative Adversarial Networks (GANs, introduced by Ian Goodfellow in 2014) and latent diffusion models. Techniques like Reinforcement Learning from Human Feedback (RLHF) align model outputs with human intent.