ASA Section on Text Analysis Blog

  GPT (Generative Pre-trained Transformer) models are a specific type of LLM (Language Model) that uses the Transformer architecture and pre-training techniques to generate text. LLMs, on the other hand, refer to a broader category of models that can perform various language-related tasks, such as text classification, question-answering, and machine translation. GPT models are just one type of LLM, and there are other types of LLMs that do not use the Transformer architecture or pre-training techniques. That said, the most used LLMs today are of the type GPT, so we want to describe the way GPT models work. This concept of generative AI has opened up new possibilities in various fields, such as natural language processing, computer vision, and machine learning.  Generative In this introduction, we will explore the concept of  generative  in GPT models.  Generative GPT models are capable of generating human-like text, images, and even code. The term "generati...

  Over the past few years, modern LLMs have gained attention and are causing a revolution in life as we know it. These models have shown high accuracy (both recall and precision) in various NLP tasks such as language translation, text summarization, and question-answering. This primer aims to provide an overview of LLMs, covering topics such as data pre-processing, model architecture, training and optimization, evaluation metrics, and current applications, written for the statistician. It is essential to understand the statistical foundations of LLMs in order to apply their strengths and appreciate their weaknesses. While LLMs are not built from statistics in the traditional sense, they are deeply connected to statistical methods and principles. LLMs are a product of machine learning, a subset of artificial intelligence that relies on statistical learning techniques to make predictions or decisions without being explicitly programmed to perform the task. As part of this primer,...