The article also explains the concept of word vectors, which are the way language models represent words. Instead of using a sequence of letters like humans do, language models use a long list of numbers, or a "word vector," to represent a word. This method allows for reasoning about relationships between words, similar to how spatial relationships can be reasoned about using coordinates. The article aims to demystify the workings of LLMs without using technical jargon or advanced math.
Key takeaways:
- Large language models (LLMs) like ChatGPT are trained to predict the next word in a sequence and require huge amounts of text for this training.
- LLMs are built on neural networks and trained using billions of words of ordinary language, making their inner workings complex and not fully understood yet.
- Language models represent words using a long list of numbers called a "word vector", which is useful for reasoning about relationships between words.
- The transformer is the basic building block for systems like ChatGPT, and good performance of these models requires large quantities of data.