Introduction to Large Language Models
Get introduced to language models and large language models.
Large language models (LLMs) represent a peak in natural language processing, revolutionizing how machines comprehend and respond to language. These models, trained on vast datasets, have demonstrated an unprecedented ability to capture context and nuance in text, enabling applications ranging from sophisticated chatbots and content creation to advanced language translation and code generation.
LLMs are models designed to understand and generate human-like text on a large scale. These models use deep learning techniques, particularly architectures like transformer neural networks. The term “large” in LLMs refers to the vast amount of parameters and data these models are trained on, allowing them to capture intricate patterns, context, and semantic relationships within language. Let’s imagine a conversation with a friend, where the friend starts a sentence with: “I’m going to make a cup of ________.” Humans would likely predict that the next word could be coffee or tea based on their knowledge of common beverage choices.
Similarly, a language model is trained to understand and predict the next word in a sequence based on the context of the preceding words. It learns from vast amounts of text data and can make informed predictions about what word will likely come next in a given context.
Before going into more detail, let’s first discuss what language models are.
Language models
A language model (LM) can be defined as a probabilistic model that assigns probabilities to sequences of words or tokens in a given language. The goal is to capture the structure and patterns of the language to predict the likelihood of a particular sequence of words.
Let’s assume we have a vocabulary