Comment by bee_rider | Hacker News Reader

Comment by bee_rider | Hacker News Reader

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What would those probabilities mean in the context of these modern LLMs? They are basically “try to continue the phrase like a human would” bots. I imagine the question of “how good of an approximation is this to something a human might write” could possibly be answerable. But humans often write things which are false.

The entire universe of information consists of human writing, as far as the training process is concerned. Fictional stories and historical documents are equally “true” in that sense, right?

Hmm, maybe somehow one could score outputs based on whether another contradictory output could be written? But it will have to be a little clever. Maybe somehow rank them by how specific they are? Like, a pair of reasonable contradictory sentences that can be written about the history-book setting indicate some controversy. A pair of contradictory sentences, one about history-book, one about Narnia, each equally real to the training set, but the fact that they contradict one another is not so interesting.

> But humans often write things which are false.

LLMs do it much more often. One of the many reasons in the coding area is the fact that they're trained on both the broken and working code. They can propose as a solution a piece of code that was taken verbatim from "why is this code not working" SO question.

Google decided to approach this major problem by trying to run the code before giving the answer. Gemini doesn't always succeed as it might not have all packages needed installed for example, but at least it tries, and when it detects bullshit, it tries do correct that.

> But humans often write things which are false.

Not to mention, humans say things that make sense for humans to say and not a machine. For example, one recent case I saw was where the LLM hallucinated having a Macbook available that it was using to answer a question. In the context of a human, it was a totally viable response, but was total nonsense coming from an LLM.

It’s interesting because often the revolution of LLM is compared to the calculator but a calculator that does a random calculation mistake would never have been used so much in critical systems. That’s the point of a calculator, we never double check the result. But we will never check the result of an LLM because of the statistical margin of error in the feature.

Right: When I avoid memorizing a country's capital city, that's because I can easily know when I will want it later and reliably access it from an online source.

When I avoid multiplying large numbers in my head, that's because I can easily characterize the problem and reliably use a calculator.

Neither are the same as people trying to use LLMs to unreliably replacing critical thinking.

> But we will never check the result of an LLM because of the statistical margin of error in the feature.

I don't follow this statement: if anything, we absolutely must check the resut of an LLM for the reason you mention. For coding, there are tools that attempt to check the generated code for each answer to at least guarantee the code runs (whether it's relevant, optimal, or bug-free is another issue, and one that is not so easy to check without context that can be significant at times).

I mean I do check absolutely everything an LLM outputs. But following the analogy of the calculator, if it goes that way, no one will in the future check the result of an LLM. Just like no one ever checks the result of a complex calculation. People get used to the fact that a large percentage of the time it’s correct. That might allow big companies to manipulate people because a calculator is not plugged to the cloud to falsify the results depending on who you are and make your projects fail

I see a whole new future of cyber warfare being created. It'll be like the reverse of a prompt engineer: an injection engineer. Someone who can tamper with the model just enough to sway a specific output that causes <X>.

That’s a terrifying future and even more so because it might already be in route

The critical difference is that (natural) language itself is in the domain of statistical probabilities. The nature of the domain is that multiple outputs can all be correct, with some more correct than others, and variations producing novelty and creative outputs.

This differs from closed-form calculations where a calculator is normally constrained to operate--there is one correct answer. In other words "a random calculation mistake" would be undesirable in a domain of functions (same input yields same output), but would be acceptable and even desirable in a domain of uncertainty.

We are surprised and delighted that LLMs can produce code, but they are more akin to natural language outputs than code outputs--and we're disappointed when they create syntax errors, or worse, intention errors.

LLMs already have a confidence score when printing the next token. When confidence drops, that can indicate that your session has strayed outside the training data.

Re:contradictory things: as LLM digest increasingly large corpuses, they presumably distill some kind of consensus truth out of the word soup. A few falsehoods aren’t going to lead it astray, unless they happen to pertain to a subject that is otherwise poorly represented in the training data.

I hope they can distill this consensus truth, but I think it is a tricky task; I mean human historians even still have controversies.

> What would those probabilities mean in the context of these modern LLMs?

They would mean understanding the sources of the information they use for inference, and the certainty of steps they make. Consider:

- "This conclusion is supported by 7 widely cited peer-reviewed papers [list follows]" vs "I don't have a good answer, but consider this idea of mine".

- "This crucial conclusion follows strongly from the principle of the excluded middle; its only logical alternative has been just proved false" vs "This conclusion seems a bit more probable in the light of [...], even though its alternatives remain a possibility".

I suspect that following a steep gradient in some key layers or dimensions may mean more certainty, while following an almost-flat gradient may mean the opposite. This likely can be monitored by the inference process, and integrated into a confidence rating somehow.

I don’t think I disagree with your general point, but this is fairly different from what the comment above was looking for—confidence values that we can put next to our outputs.

I mean, I don’t think such a value (it is definitely possible I’m reading it overly-specifically), like, a numerical value, can generally be assigned to the truthiness of a snippet of general prose.

I mean, in your “7 peer reviewed papers” example, part of the point of research (a big part!) is to eventually overturn previous consensus views. So, if we have 6 peer reviewed papers with lots of citations, and one that conclusively debunks the rest of them, there is not a 6/7 chance that any random sentiment pulled out of the pile of text is “true” in terms of physical reality.

Interesting point.

You got me thinking (less about llms, more about humans), that adults do have many contradictory truths, some require nuance, some require completely different mental compartment.

Now I feel more flexible about what truth is, as a teen and child I was more stuborn, sturdy.

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