The early days of natural language processing saw researchers experiment with many different approaches, including conceptual ontologies and rule-based systems. While some of these methods proved narrowly useful, none yielded robust results. That changed in the 2010s when NLP research intersected with the then-bustling field of neural networks. The collision laid the ground for the first large language models. This post, adapted and excerpted from one on Snorkel.ai entitled “Large language models: their history, capabilities, and limitations,” follows the history of LLMs from that first intersection to their current state.
