Comment by ACCount37
7 hours ago
We live in a non-deterministic world. Anything "deterministic" in it is a castle built on quicksand.
LLMs are, as far as the nastiness of the Real World goes, really fucking benign. Future models outperform past models, both in open weight land and at the big frontier labs. Performance per $ only ever goes up. That's just nice.
> We live in a non-deterministic world. Anything "deterministic" in it is a castle built on quicksand.
Except the Enterprise, and a lot of what people want compute for, is built on deterministic systems or processes. I'm not saying the non-deterministic nature of LLMs isn't useful. However I've worked with a lot of organizations on SOAR projects, for example. When you can weave the deterministic and non-deterministic together you get a relatively efficient system. A workflow that will stay on the rails and will come to a conclusion as expected. And the "as expected" part is critical in these types of systems. The reality of, using SOAR as an example, is also that most enterprise would be much better served by fast SLMs. Parse an email and validate if it's SPAM / Phishing or read a chunk of firewall logs and look for outliers / indications for escalation - those things can get messy in a deterministic system because of potentially unstructured data.
I don't believe it's either / or. And I believe that LLMs just aren't efficient, fast or reliable in the sense that deterministic are. It seems, at least to me, a better together story.
I think it might be built on something more than deterministic systems. Some property that is a subset of deterministic, so all your argument still apply, but merely being deterministic is not good enough.
LLMs are what made me start considering this. Imagine a company using an LLM that was fully deterministic. All RNG was either removed or seeded in such a way that the same input (so many the seed counts as part of the input) gave the exact same output. Fully deterministic.
But such an LLM, with a slight drift in input, could still produce very different outputs. This isn't being non-deterministic, but more than the change in outputs does not naturally follow from the input. I'm thinking like how 2 double pendulums can (but not always do) greatly diverge given a very small change in their input.
So in light of that I've begun to call this new property non-chaotic. So Enterprise depends on non-chaotic systems, which are a subset of deterministic systems, and then wrangling the chaotic elements they cannot remove as much as possible.
The follow question I now have is if all LLMs are inherently chaotic, or if it is possible to have a non-chaotic LLM.
YES, but you seem to not understand that having two non-deterministic layers is incompatible. #1 is fine: it has random issue and you build around those random issues; those issues don't change unless you change them.
#2 is not fine; that non-determinism you do not control, have no insight into, etc.
I'm saying sure, give me #1 if it means I can build a harness around it and smooth over the edges. But I'm not taking #1 and #2. There's zero reasonable way to manae two non-deterministic systems.