[illusionist123]

I think it's not possible to get rid of hallucinations given the structure of LLMs. Getting rid of hallucinations requires knowing how to differentiate fact from fiction. An analogy from programming languages that people might understand is type systems. Well-typed programs are facts and ill-typed programs are fictions (relative to the given typing of the program). To eliminate hallucinations from LLMs would require something similar, i.e. a type system or grammar for what should be considered a fact. Another analogy is Prolog and logical resolution to determine consequences from a given database of facts. LLMs do not use logical resolution and they don't have a database of facts to determine whether whatever is generated is actually factual (or logically follows from some set if facts) or not, LLMs are essentially Markov chains and I am certain it is impossible to have Markov chains without hallucinations.

So whoever is working on this problem, good luck because you have you have a lot of work to do to get Markov chains to only output facts and not just correlations of the training data.

[blackkettle]

While I'm definitely not going to argue that LLMs are inherently 'thinking' like people do, one thing I do find pretty interesting is that all this talk about hallucinations and bias seems to often conveniently ignore the fact that people are often even more prone to these exact same problems - and as far as I know that's also unlikely to be solved.

ChatGPT is often 'confidently wrong' - I'm pretty sure I've been confidently wrong a few times too, and I've met a lot of other people in my life who've express that trait from time to time too, intentionally or otherwise.

I think there is an inherent trade off between 'confidence', 'expression', and of course 'a-priori bias in the input'. You can learn to be circumspect when you are unsure, and you can learn to better measure your level of expertise on a subject.

But you can't escape that uncertainty entirely. On the other hand, I'm not very convinced about efforts to train LLMs on things like mathematical reasoning. These are situations where you really do have the tools to always produce an exact answer. The goal in these types of problems should focus not on holistically learning how to both identify and solve them, but exclusively on how to identify and define them, and then subsequently pass them off to exact tools suitable for computing the solution.

[StackOverlord]

Indeed but people who are confidently wrong in good faith so to speak don't make up science paper titles.

[famouswaffles]

LLMs already know how to distinguish fact from fiction much better than random chance and the base non-RLHF GPT-4 model was excellently calibrated (its predicted confidence in an answer generally matches the probability of being correct). "Eliminating" it is not that important. Getting it to human levels is the goal. and boy do humans often "hallucinate", i.e have a poor grasp of what they do or do not know and confidently spout nonsense.

[illusionist123]

It doesn't matter what humans do or do not do. Human performance as a benchmark is not a useful metric for what machines should or should not do.

[famouswaffles]

It very much does matter sorry.

[camus_absurd]

“Fake it till you make it”

[StackOverlord]

A type system is for internal consistency though. Facts are about external consistency with real world data. And even then facts are always a social augmentation in that they are always captured in a given social context, and by that I include the lenses of theoretical frameworks and axiomatics. They always have a spin they can lose when considered from another standpoint and at the very minimum they are conditioned by attention and relevancy, and it has everything to do with our current representation of the world and nothing with the world itself.

[visarga]

Technically, transformers condition on the entire past not just the last step, but RNNs are Markov Chains. RNNs have information bottleneck issues though.

[uoaei]

Transformers (for NLP) also perform steps on Markov chains. The difference is that with transformers (for NLP), which Markov chain it's moving along changes every step.