It's [contraint: clever remark]!

Modern LLMs are impressive pieces of machinery, capable of feats across many domains that were previously thought to be unique signifiers of human intelligence. This power and generality comes from their incredible size, which allows LLMs to compress huge quantities of information and recognize highly sophisticated patterns.

Almost exactly as easy as it sounds

One of the most powerful arguments for incorporating deep learning models into your workflow is the possibility of transfer learning: using a pre-trained model’s latent representations as a starting point for your own modeling task. This can be particularly useful when you have a fairly small number of labeled examples, but the task in question is similar to a pre-existing model’s task. So how easy is it to do transfer learning with an LLM? As we’ll see, with HuggingFace’s transformers library, it’s actually quite easy.

Or, how to build bad implicit factor models

Nets work great for problems involving lots of high-dimensional data (such as image or text problems). But, they often struggle for problems with low-dimensional inputs, particularly in cases where the response is not especially smooth with respect to the input. In this post, we’ll explore one way to get around this using “Fourier encodings”, and tackle a couple of interesting applications of this technique, including as a tool for portfolio construction.