I feel anthropomorphism is part of the marketing strategy for LLMs
I've seen "bullshitting" suggested, but this of course still implies intent, which AIs do not have in any typical sense of the word.
I think we as a community have settled on hallucination as the best English word that approximately conveys the idea. I've seen folks on here making up words to describe it, as if that is any more useful to the victim here. The victim being the uninformed (w.r.t AI tech) layperson.
In humans hallucination is about a loss of a relationship with an underlying physical world. A physical world whose model we have in our heads and interact with in intentional ways if we are not hallucinating.
That means using the word hallucinating implies that the thing could also not be hallucinating and have a grip on reality. And rhis was my criticism, a LLM spits out plausible phrases, if the graph wouldn't consider an output plausible it wouldn't return it. That means for the LLM there is no difference between plausible bogus and a factually correct statement, this is something humans interpret into the output from the outside.
Human do this too, of course. The LLMs are simply emulating this human behavior.
It’s a better alternative than “bullshitting”, because “confabulating” does not have that kind of connotation of intent.
Wrong or inaccurate are alternatives.
So far this has mostly been done using Reinforcement Learning, but catching it and doing it inference seems like it could be interesting to explore. And much more approachable for open source, only the big ML labs can do this sort of RL.
If probability(sum(tokens[:5])) < 0.5: Respond("I'm sorry I don't quite understand what you mean.")
The article itself is uncertainty at the level of the next token rather than of the entire response, which is different: "Capital of Germany is" followed by "Berlin" is correct but it would have also been valid for the full answer to have been ", since reunification in 1990, Berlin; before this…" - correct at the conceptual level, uncertainty at the token level.
Most of the users aren't aware of the maths and use words in more every-day manners, to the annoyance of those of us who care about the precise technical definitions.
The listed types of uncertainty can and do have different uses in different cases.
Especially the difference between "I don't know the answer" and "I do know absolutely that the answer is that nobody knows".
As a chatbot it's also important to say "I don't understand your question" when appropriate, rather than to say "dunno" in response to e.g. "how do I flopragate my lycanthrope?"
The article is talking about inference. Most models people are actually using have gone through RLHF or DPO. So the uncertainty at inference includes all dimensions of uncertainty. A token choice can effectively be a branch from a conceptual perspective.
It's also true that uncertainty can be decomposed into "flavours". The simplest and most discussed decomposition is into aleatoric and epistemic kinds of uncertainty. Epistemic uncertainty (or model-based uncertainty) usually refers to the case, when poor output is a result of the model being presented with the kind of input which it never saw before, and should not be expected to handle correctly. Aleatoric uncertainty on the other hand is thought to be intrinsic to the data itself, think of the natural ambiguity of the task, or noisy labelling.
People in the field of uncertainty estimation are very much concerned with developing methods of quantifying these different types of uncertainty, and different methods can be more sensitive to one or the other.
I think there's a human tendency to reduce the problem one has answering a given question to a question of just "uncertainty" and so we look at LLM answers as involving just single level of uncertainty. But that's anthropomorphism.
AI images (and photograph before it) showed us new, unimagined ways an image can be wrong (or rather, real-seaming but wrong). AI language interactions do this too but in a more subtle way.