#LLMs have really created a paradigm shift in machine learning. It used to be so that you would train an #ML model to perform a task by collecting a dataset reflecting the task, with task output labels, and then using supervised learning to learn this task by doing.
Now a new paradigm has emerged: Train by reading about the task. We have such generalist models that we can let them learn about the domain by reading all the books and other content about it, and then utilize that learned knowledge to perform the task. Note that task labels are missing. You might need those to measure the performance but you don't need those for training.
Of course if you have both example performances as task labels and lots of general material about the topic, you can actually use both to get even better performance.
Here is a good example of training the model not by example performances, but by general written knowledge about the topic. #GPT4 surpasses the quality levels of previous state-of-the-art despite not having been trained for this task.
This is the power of generalist models; they unlock new ways to train them, which for example allow us to surpass human-level by side-stepping imitative objectives. This isn't the only way to train skills these models enable, there are countless other ways, but this is an uncharted territory.
The classic triad of supervised learning, unsupervised learning and reinforcement learning are going to have an explosion of new training methodologies to become their peers because of this.
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