LLMs evolved from early linguistic theories and statistical models to today's transformer-based architectures. Initially, rule-based systems aimed to interpret human language, but their limitations prompted innovations in neural networks. Key milestones include the introduction of Word2Vec, which transformed how words are represented in vector spaces, and the arrival of the Transformer model in 2017, which allowed for unprecedented scalability and efficiency. These advances laid the groundwork for sophisticated models like BERT and GPT, expanding the boundaries of what AI can achieve in text generation and understanding.
**Key takeaway:**