Jonasb

Humans are amazing. And–let's be honest–pretty weird. I mean, why are so many of us all hyped up about Large Language Models (LLMs)? How did we collectively decide this kind of automated decision-making is "the next big thing"? It's not like a talking thesaurus can change the world, right?* The thing most people seem to miss is that LLMs don't understand humans. They can generate high-quality content, true, and some of them are already in the top 95th percentile when it comes to processing text, video, medical data etc. But they have no idea what a human "is". Don't get me wrong, I think LLMs are an amazing technology–I've been working with language models since 2017–but I am also quite sceptical about the world-changing potential these models have. So I thought it would be good to do a deep dive into how LLMs learn. Let's dive right in. Part one: Training Large Language Models To start, like any other machine learning model, LLMs learn from examples. These examples are selected by humans based on their ability to teach the model something about the task or tasks that need to be automated. For example, if a machine learning researcher is training a model that needs to generate text, he or she will feed the model text examples. Researchers have worked on different combinations of inputs and outputs based on the success of early LLMs. As a result we now have models that * ... can generate images from text. They are shown examples of text as input, and examples of images as output. * ... can generate translations. They are shown examples of text in one language as an input, and a (human-) translated version of that same text as output. * ... can decipher proteins. They are shown images of protein structures as input, and mapped-out components of these structures as output. You get the picture. The sum total of the examples shown to a model is called its "training data". People working on a model will tell it what to learn by configuring the predictio