Abstracts: July 18, 2024
MITRA: So the post-training phase is very important for language models. You can really improve the model a lot by creating high-quality synthetic data. The problem is, however, though, high-quality synthetic data creation requires lots of human effort and expertise. The problem that we’re trying to tackle is, how do you reduce human effort? How can you create high-quality data with really low amount of human effort? When you have a language model and, let’s say, you want to apply it somewhere, you might have to train a generic model before. Which could be small or big. Doesn’t matter. After that, you can specialize it on the domain that you are looking for, and when you want to do that—to make it really fast, this particular process—it’s best if