Most plausible world model is not something stored raw in utterances. What we interpret from sentences is vastly different from what is extractable from mere sentences on their own.

Facts, unlike fabulations, require crossing experience beyond the expressions on trial.

> The factual answer is traditionally the most plausible response to many questions

Except in cases where the training data is more wrong than correct (e.g. niche expertise where the vox pop is wrong).

However, an LLM no more deals in Q&A than in facts. It only typically replies to a question with an answer because that itself is statistically most likely, and the words of the answer are just selected one at a time in normal LLM fashion. It's not regurgitating an entire, hopefully correct, answer from someplace, so just because it was exposed to the "correct" answer in the training data, maybe multiple times, doesn't mean that's what it's going to generate.

In the case of hallucination, it's not a matter of being wrong, just the expected behavior of something built to follow patterns rather than deal in and recall facts.

For example, last night I was trying to find an old auction catalog from a particular company and year, so thought I'd try to see if Gemini 3 Pro "Thinking" maybe had the google-fu to find it available online. After the typical confident sounding "Analysing, Researching, Clarifying .." "thinking", it then confidently tells me it has found it, and to go to website X, section Y, and search for the company and year.

Not surprisingly it was not there, even though other catalogs were. It had evidently been trained on data including such requests, maybe did some RAG and got more similar results, then just output the common pattern it had found, and "lied" about having actually found it since that is what humans in the training/inference data said when they had been successful (searching for different catalogs).