[_yb2s]

"Hallucinations" are the creative aspect of LLMs, which is what they are more useful for- if anything we want more of them. We already have much simpler systems that search and regurgitate facts. We need more intelligent hallucinations that are consistent with and extend rather than conflict with the data.

[thwarted]

Is it even possible to measure and distinguish the output as being hallucinated or not? All LLM output is hallucinated, it's only by statistics or chance that some of the output reflects facts, and we're only able to make that assessment because we can compare the output to facts. The model can't make that assessment itself.

Going from 50% "accurate" to 90% "accurate" may actually be more insidious because it changes the utility from being a coin flip to trying to determine which 10% is inaccurate, or downplaying the existence of inaccuracies because at 90% it is "mostly correct".

[MattPalmer1086]

I would argue with your definition of hallucination here. It's just a value judgement we humans apply to output that does not correspond to reality in some way that we don't find useful.

We can control how diverse or creative output is via temperature. I am assuming that these new models could work at higher temperatures (i.e. more creative) while maintaining factual accuracy for things you care about. Or alternatively, keep the same temperature but have far fewer hallucinations (i.e. wrong answers).