I ask for confidence scores in my custom instructions / prompts, and LLMs do surprisingly well at estimating their own knowledge most of the time.
It’s tempting to think of a language model as a shallow search engine that happens to output text, but that metaphor doesn’t actually match what’s happening under the hood. A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is traverse a high‑dimensional statistical manifold of language usage, trying to produce the most plausible continuation.
That’s why a confidence number that looks sensible can still be as made up as the underlying output, because both are just sequences of tokens tied to trained patterns, not anchored truth values. If you want truth, you want something that couples probability distributions to real world evidence sources and flags when it doesn’t have enough grounding to answer, ideally with explicit uncertainty, not hand‑waviness.
People talk about hallucination like it’s a bug that can be patched at the surface level. I think it’s actually a feature of the architecture we’re using: generating plausible continuations by design. You have to change the shape of the model or augment it with tooling that directly references verified knowledge sources before you get reliability that matters.
When trained on chatting (a reflection system on your own thoughts) it mostly just uses a false mental model to pretend to be a desperate intelligence.
Thus the term stochastic parrot (which for many us actually pretty useful)
I remain highly skeptical of this idea that it will replace anyone - the biggest danger I see is people falling for the illusion. That the thing is intrinsically smart when it’s not - it can be highly useful in the hands of disciplined people who know a particular area well and augment their productivity no doubt. Because the way we humans come up with ideas and so on is highly complex. Personally my ideas come out of nowhere and mostly are derived from intuition that can only be expressed in logical statements ex-post.
These comparisons are becoming really annoying to read.
You use the word “plausible” instead of “correct.”
As someone else put it well: what an LLM does is confabulate stories. Some of them just happen to be true.
That’s like saying linear regression produces plausible results. Which is true but derogatory.
It’s amazing that experts like yourself who have a good grasp of the manifold MoE configuration don’t get that.
LLMs much like humans weight high dimensionality across the entire model then manifold then string together an attentive answer best weighted.
Just like your doctor occasionally giving you wrong advice too quickly so does this sometimes either get confused by lighting up too much of the manifold or having insufficient expertise.
Of the 8, 3 were wrong, and the references contained no information about pin outs whatsoever.
That kind of hallucination is, to me, entirely different than what a human researcher would ever do. They would say “for these three I couldn’t find pinouts” or perhaps misread a document and mix up pinouts from one model for another.. they wouldn’t make up pinouts and reference a document that had no such information in it.
Of course humans also imagine things, misremember etc, but what the LLMs are doing is something entirely different, is it not?
Really? When I search for cases on LexisNexis, it does not return made-up cases which do not actually exist.
Since your example comes from the legal field, you'll probably very well know that even well intentioned witnesses that don't actively try to lie, can still hallucinate all kinds of bullshit, and even be certain of it. Even for eye witnesses, you can ask 5 people and get several different incompatible descriptions of a scene or an attacker.
Context matters. This is the context LLMs are being commercially pushed to me in. Legal databases also inherit from reality as they consist entirely of things from the real world.
Huh? Are you arguing that we still live in a pre-scientific era where there’s no way to measure truth?
As a simple example, I asked Google about houseplant biology recently. The answer was very confidently wrong telling me that spider plants have a particular metabolic pathway because it confused them with jade plants and the two are often mentioned together. Humans wouldn’t make this mistake because they’d either know the answer or say that they don’t. LLMs do that constantly because they lack understanding and metacognitive abilities.
No. A strange way to interpet their statement! Almost as if you ...hallucinated their intend!
They are arguing that humans also hallucinate: "LLMs much like humans" (...) "Just like your doctor occasionally giving you wrong advice too quickly".
As an aside, there was never a "pre-scientific era where there [was] no way to measure truth". Prior to the rise of modern science fields, there have still always been objective ways to judge truth in all kinds of domains.
You, on the other hand, truly have never encountered any information about Thai grammar or (surprisingly) hot to build a jet turbine. (I can explain in general terms how to build one from just watching Discovery channel)
The difference is that the models actually have some information on those topics.
And is that that different than what we do under the scenes? Is there a difference between an actual fact vs some false information stored in our brain? Or both have the same representation in some kind of high‑dimensional statistical manifold in our brains, and we also "try to produce the most plausible continuation" using them?
There might be one major difference is at a different level: what we're fed (read, see, hear, etc) we also evaluate before storing. Does LLM training do that, beyond some kind of manually assigned crude "confidence tiers" applied to input material during training (e.g. trust Wikipedia more than Reddit threads)?
Don't know about that, bullshitting is a thing. Especially online, where everybody pretends to be an expert on everything, and many even believe it.
But even if so, is that because of some fundamental difference between how a human and an LLM store/encode/retrieve information, or more because it has been instilled into a human through negative reinforcement (other people calling them out, shame of correction, even punishment, etc) not to make things up?
"Return a score of 0.0 if ...., Return a score of 0.5 if .... , Return a score of 1.0 if ..."
I read a comment here a few weeks back that LLMs always hallucinate, but we sometimes get lucky when the hallucinations match up with reality. I've been thinking about that a lot lately.
Kind of. See e.g. https://openreview.net/forum?id=mbu8EEnp3a, but I think it was established already a year ago that LLMs tend to have identifiable internal confidence signal; the challenge around the time of DeepSeek-R1 release was to, through training, connect that signal to tool use activation, so it does a search if it "feels unsure".
Their point was the 9.8 model is good enough for most things on Earth, the model doesn't need to be perfect across the universe to be useful.
G is the gravitational constant which is also universally true(erm... to the best of our knowledge), g is calculated using gravitational constant.
g, the net acceleration from gravity and the Earth's rotation is what is 9.8m/s² at the surface, on average. It varies slightly with location and altitude (less than 1% for anywhere on the surface IIRC), so "it's 9.8 everywhere" is the model that's wrong but good enough a lot of the time.