So correct me if I’m wrong here but wouldn’t another way to look at this be something like re-compressing a JPEG? Each time you compress a compressed jpeg you s

So correct me if I’m wrong here but wouldn’t another way to look at this be something like re-compressing a JPEG? Each time you compress a compressed jpeg you strip more and more information out of it? Same with any lossy compression, really. These LLM’s are inherently a bit like lossy compression algorithms. They take information and pack it in a way that keeps its essence around (at least that is the plan). But like any lossy compression, you cannot reconstruct the original. Training a lossy compression scheme like an LLM using its own data is just taking that already packed information and degrading it. I hope I’m right framing it this way because ultimately that is partly what an LLM is, it’s a lossy compression of “the entire internet”. A lossless model that can be queried like an LLM would be massive, slow and probably impossible with today’s tech. I suspect that we will develop new information theory that mathematically proves these things can’t escape the box they were trained in, meaning they cannot come up with new information that isn’t already represented in the relationships between the various bits of data they were constructed with. They can “only” find new ways to link together the information in their corpus of knowledge. I use “only” in quotes because simply doing that alone is pretty powerful. It’s connecting the dots in ways that haven’t been done before. Honestly the whole LLM space is cool as shit when you really think about it. It’s both incredibly overhyped yet very under hyped at the same time.