preface

I’ve always been fascinated with language models. More than a decade ago, my journey into AI began with a statistical pattern classification class, which led to my first independent project: developing a model and web application to detect the mood of a song based on its lyrics.

Fast forward to 2022, with the release of ChatGPT, large language models (LLMs) have taken the world by storm and have revolutionized how many of us work. These models are incredibly versatile, aiding in tasks such as checking grammar, composing emails, summarizing lengthy documents, and much more. This is owed to their ability to parse and generate human-like text, which is important in various fields, from customer service to content creation, and even in more technical domains like coding and data analysis.

As their name implies, a hallmark of LLMs is that they are “large”—very large—encompassing millions to billions of parameters. (For comparison, using more traditional machine learning or statistical methods, the Iris flower dataset can be classified with more than 90% accuracy using a small model with only two parameters.) However, despite the large size of LLMs compared to more traditional methods, LLMs don’t have to be a black box.