Thanks for pointing out the elephant in the room with LLMs.
The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
You can't blame an LLM for getting the facts wrong, or hallucinating, when by design they don't even attempt to store facts in the first place. All they store are language statistics, boiling down to "with preceding context X, most statistically likely next words are A, B or C". The LLM wasn't designed to know or care that outputting "B" would represent a lie or hallucination, just that it's a statistically plausible potential next word.
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).
Same for human knowledge though. Learn from society/school/etc that X is Y, and you repeat X is Y, even if it's not.
>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.
And how is that different than how we build up an answer? Do we have a "correct facts" repository with fixed answers to every possibly question, or we just assemble our training data from a weighted graph (or holographic) store of factoids and memories, and our answers are also non deterministic?
Humans use language to express something (facts, thoughts, etc), so you can consider these thoughts being expressed as a bias to the language generation process, similar perhaps to an image being used as a bias to the captioning part of an image captioning model, or language as a bias to an image generation model.
My point however is more that the "thoughts being expressed" are themselves being generated by a similar process (and that it's either that or a God-given soul).
So, with the LLM all you have is the auto-regressive language prediction loop.
With animals you primarily have the external "what happens next" prediction loop, with these external-world fact-based predictions presumably also the basis of their thoughts (planning/reasoning), as well as behavior.
If it's a human animal who has learned language, then you additionally have an LLM-like auto-regressive language prediction loop, but now, unlike the LLM, biased (controlled) by these fact-based thoughts (as well as language-based thoughts).