I honestly have no idea why OAI felt that they needed to publish a “paper” about this, since it is blazingly obvious to anyone who understands the fundamentals of transformer inference, but here we are.
The confusion on this topic comes from calling these suboptimal outputs “hallucinations” which drags anthropomorphic fallacies into the room by their neck even though they were peacefully minding their own business down the corridor on the left.
“Hallucination” implies a fundamentally fixable error in inference, a malfunction of thought caused by a pathology or broken algorithm.
LLMs “Hallucinating” are working precisely as implemented, only we don’t feel like the output usefully matches the parameters from a human perspective. It’s just unhelpful results from the algorithm, like any other failure of training, compression, alignment, or optimisation.
A: probably work around by prompting and properly structuring tasks
B: never completely rule out
C: not avoid at all in certain classes of data transformations where it will creep in in subtle ways and corrupt the data
D: not intrinsically detect, since it lacks the human characteristic of “woah, this is trippy, I feel like maybe I’m hallucinating “
These misconceptions stem from the fact that in LLM parlance, “hallucination” is often conflated with a same-named, relatable human condition that is largely considered completely discrete from normal conscious thought and workflows.
Words and their meanings matter, and the failure to properly label things often is at the root of significant wastes of time and effort. Semantics are the point of language.
The analogy may be much less wishy-washy than you imagine: check out "Source-aware training"
https://arxiv.org/abs/2404.01019
Everybody quickly learns to take note of who-claims-what: in kindergarten kids learn to detect each others lies, and learn to attach more credibility to the teachers / caretakers than dubious claims "I am allowed to hit you", to the point that precisely because they learn it they start crafting insidious lies "the teacher said I'm allowed to hit you", after which you learn to be skeptical of meta-claims etc.
Sadly no SOTA LLM's use source-aware training.
Perhaps start a petition on
change.org openpetition.eu openpetition.org
etc.
We already know that larger models hallucinate less since they can store more information, are there any smaller models which hallucinate less
excerpt: Claim: Avoiding hallucinations requires a degree of intelligence which is exclusively achievable with larger models. Finding: It can be easier for a small model to know its limits. For example, when asked to answer a Māori question, a small model which knows no Māori can simply say “I don’t know” whereas a model that knows some Māori has to determine its confidence. As discussed in the paper, being “calibrated” requires much less computation than being accurate.
They are merely an output that we find unuseful, but in all other ways is optimal based on the training data, context, and model precision and parameters being used.