Today's Large Language Models Are Essentially BS Machines

I'll believe this when the models stop looking like machine translation did six years ago.

During the big GPT-4 news cycle I think a bunch of folks posted claims that were outrageously good- "language model passes medical exams better than humans", etc. When I looked into them, in nearly all cases, the claims were boosted far beyond the reality. And the reality seemed much more consistent with a fairly banal interpretation: LLMs produce realistic looking text but have no real ability to distinguish truth from fabrication (which is a step beyond bullshit!).

The one example that still interests me is math problem solving. Can next-token predictors really solve generalized math problems as well as children? https://arxiv.org/abs/2110.14168

While there is truth here, they can be quite effective as a logic engine vs a fact engine. One of the most popular LLM use cases is retrieval augmented generation (RAG), where the LLM is limited by a provided context.

Do you need 7B/13B/33B/77B parameters to do this? That is a question up for debate and something I'm exploring with the concept of micro/nano models (https://neuml.hashnode.dev/train-a-language-model-from-scrat...). There is the sense that today's LLMs could be overkill for a problem such as RAG.

I like to think of all responses from LLM's like the top-rated post on Stack Overflow or a top five blog post from a Google search. It's helpful information that _may_ be correct but needs to be verified. A lot of the time, it's spot on. Some percentage of the time, it's straight up incorrect. You have to be willing to compare various sources of data and find what's accurate. It's a nice, easy-to-use starting point, essentially.

"The internet is simply too slow to be useful".