That’s right, no more waiting hours for your AI to spit out a response.

The authors of this paper (who are all super smart and probably have PhDs in computer science) figured out that by using some fancy math tricks called “quantization” and “pruning,” they could significantly reduce the size and complexity of these models without losing any of their awesomeness. And since smaller models run faster, voila! Instant speed boost.

But here’s where it gets really cool: instead of just shrinking down the model itself (which would be like trying to fit a square peg into a round hole), they also optimized the way that the model processes data. This involved breaking up the input text into smaller chunks and feeding them through the model one at a time, which not only sped things up but also made it easier for the model to understand what’s going on (since it doesn’t have to process everything at once).

So if you’re wondering how this all works in practice, let me give you an example. Let’s say you ask your LLM a question like “What is the capital of France?” Instead of trying to answer that question by reading through every single book ever written (which would take forever), the model breaks it down into smaller chunks and processes them one at a time. First, it reads the word “capital,” which tells it that we’re looking for some kind of location or city. Then it reads the word “of,” which helps it understand that this is part of a larger sentence (like “What is the capital of France?”). And so on and so forth until it has processed all of the relevant information and can give you an answer.

But here’s where things get really interesting: instead of just spitting out a response like “Paris,” the model also provides some context or explanation for why that answer is correct (which is what we call “post-hoc explanations”). This might involve breaking down the sentence into smaller parts and explaining how each one contributes to the overall meaning, or it might involve using other sources of information (like a dictionary or a map) to help clarify things.

So if you’re wondering why this is such a big deal, let me put it in simpler terms: by making LLMs faster and more accurate without sacrificing their ability to understand complex language, we can use them for all kinds of cool stuff like chatbots, virtual assistants, and even medical diagnosis. And since these models are getting better every day (thanks to advances in machine learning and artificial intelligence), who knows what kind of amazing things they’ll be able to do in the future?

And if you want to learn more about this stuff, I highly recommend checking out some of the other papers that were presented at the conference (which cover topics like natural language processing, computer vision, and machine learning). Trust me it’s way cooler than it sounds!