Overcoming the limits of current LLMs

The behavior of text models is similar enough that the wording stuck, and it's not all that bad.

You can get good results by combining different models in chat, or even the same model with different parameters. Model usually gives up on hallucinations when challenged. Sometime it pushes back and provides explanation with sources.

I have a script that puts models into dialog, moderates discussion and takes notes. I run this stuff overnight, so getting multiple choices speeds up iteration.

Is this article even written by a person? Hard to know; they have a real blog with real article, but stuff like this reads strangely. Maybe it's just not a native english speaker?

> Hallucinations are certainly the toughest nut to crack and their negative impact is basically only slightly lessened by good confidence estimates and reliable citations (sources).

> The impact of contradictions in the training data.

(was this a prompt header you forget to remove?)

> LLM are incapable of "self-inspection" on their training data to find logical inconsistencies in it but in the input context window they should be able to find logical inconsistencies.

Annnnyway...

Hallucinations cannot be fixed by a good corpus in a non-deterministic (ie. temp > 0) LLM system where you've introduced a random factor.

Period. QED. If you think it can, do more reading.

The idea that a good corpus can significantly improve the error rate is an open question, but the research I've seen tends to fall on the side of "to some degree, but curating a 'perfect' dataset like that, of a sufficiently large size, is basically impossible'".

So, it's a pipe dream.

Yes, if you could have a perfect corpus, absolutely, you would get a better model.

...but how do you plan to get that perfect corpus of training data?

If it was that easy, the people spending millions and millions of dollars making LLMs would have, I guess, probably come up with a solution for it. They're not stupid. If you could easily do it, it would already have been done.

my $0.02:

This is a dead end of research, because it's impossible.

Using LLMs which are finetuned to evaluate the output of other LLMs and using multi-sample / voting to reduce the incidence of halluciations that make it past the API barrier is both actively used and far, far more effective.

(ie. it doesn't matter if your LLM hallucinates 1 time in 10; if you can reliably detect that 1 instance, sample again, and return a non hallucination).

Other solutions... I'm skeptical; most of the ones I've seen haven't worked when you actually try to use them.

Imagine a dictionary where the words are only mostly in alphabetical order. If you look up a word and don't find it, you can't be certain it's not in there. It's as useful as asking someone else, or several other people, but it's value as a reference is zero, and there's no shortage of other people on the planet.