There are a few studies that show perceived increases in productivity (all of them show negativ or almost no real increase, but I don't that is relevant to snake oils salesman).
Any higher yield comes from higher risk. If any startup feels the startup is not risky enough and really wants to have higher yield for higher risk just put the money in a Bond ETF that suits your risk appetite. Crazy that YC funds things that make a simple thing more complex and more costly for zero upside.
The bond funds offered in existing startup treasury products aren't suited for startups' long-term cash reserves. They either offer low-yield money market funds, or bond funds that aren't well suited for capital preservation on the order of months the way startups operate (see here for an example of VFSTX, the fund offered by one of the leading startup treasury products today: https://totalrealreturns.com/n/USDOLLAR,VFSTX?start=2021-01-...)
Our goal is to make sophisticated treasury management easy for startups. With Palus, they don't need to manage a brokerage account, or handle treasury ladders, or anything like that.
A robot like that would be far more useful to me than my car (I rarely use a car), and I paid 50k for my car. So for me personally, 50k would be a no-brainer. But of course only if it can do the tasks I mentioned well enough.
The LLM has an internal "confidence score" but that has NOTHING to do with how correct the answer is, only with how often the same words came together in training data.
E.g. getting two r's in strawberry could very well have a very high "confidence score" while a random but rare correct fact might have a very well a very low one.
In short: LLM have no concept, or even desire to produce of truth
Still, it might be interesting information to have access to, as someone running the model? Normally we are reading the output trying to build an intuition for the kinds of patterns it outputs when it's hallucinating vs creating something that happens to align with reality. Adding in this could just help with that even when it isn't always correlated to reality itself.
Uh, to explain what? You probably read something into what I said while I was being very literal.
If you train an LLM on mostly false statements, it will generate both known and novel falsehoods. Same for truth.
An LLM has no intrinsic concept of true or false, everything is a function of the training set. It just generates statements similar to what it has seen and higher-dimensional analogies of those .
Reasoning allows to produce statements that are more likely to be true based on statements that are known to be true. You'd need to structure your "falsehood training data" in a specific way to allow an LLM to generalize as well as with the regular data (instead of memorizing noise). And then you'll get a reasoning model which remembers false premises.
You generate your text based on a "stochastic parrot" hypothesis with no post-validation it seems.
Really, how hard is it to follow HN guidelines and :
a) not imagine straw-man arguments and not imagine more (or less) than what was said
b) refrain from snarky and false ad hominems
None of what you said in no way conflicts with what I said, and again shows a fundamental misunderstanding.
Reasoning is (mostly) part of the post-training dataset. If you add a large majority of false (ie. paradoxical, irrational etc.) reasoning traces to those, you'll get a model that successfully replicates the false reasoning of humans. If you mix it in with true reasoning traces, I imagine you'll get infinite loop behaviour as the reasoning trace oscillates between the true and the false.
The original premise that truth is purely a function of the training dataset still stands... I'm not even sure what people are arguing here, as that seems quite trivially obvious?
Ah, sorry. I haven't recognized "all the high-level capabilities of an LLM come from the training data (presumably unlike humans, given the context of this thread)" in your wording. This is probably true. LLM structure probably has no inherent inductive bias that would amount to truth seeking. If you want to get a useless LLM, you can do it. OK, no disagreement here.
The overwhelming majority of true statements isn't in the training corpus due to a combinatorial explosion. What it means that they are more likely to occur there?
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