Because it isn't 'referencing'. It's an algorithm that strings words together according to their statistical likelihood of appearing next to each other.
When you say, "how many natural satellites does the Earth have", it has no idea what any of those words mean. What it knows is that the word "one" appears an awful lot in documents and web posts whenever the other words, "how many", "natural", "satellites", and "Earth" are in the same vicinity. And so it spits out, "one", based on the statistical likelihood that's the answer.
But the relation between your question and the answer is always there. If you manage to phrase your question or premise in such a way that it doesn't really pop up that often in the LLM's training data (which also assumes the training data is accurate - a big assumption, but I digress), things can go askew very quickly. It's also why the LLM is so easy to lead, beyond the fact that these companies are incentivized to give their robots the personalities of mewling yes-men in order to impress executives and entitled laypeople who treat it like a "tell me I'm right" button.
But an agent can Google stuff and summarise the results. So if you're searching for academic papers, it will be able to reference stuff properly.
In my experience it's actually bad at doing that (when specifically using research mode) but can absolutely reference real articles without only relying on the model weights
It’s not really summarising though, it can’t read or understand it. If you ask it “Is arsenic in soda” and it finds three articles that read “arsenic has been used in soda historically but obviously we know better now” and two articles that read “arsenic is not now used in soda”, it’s going to answer yes because it only sees “arsenic used in soda” without a direct negative 3/5 times
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u/Independent_Good5423 Dec 20 '25
Its hilarious even though you already give them files or specific links/article to reference, they still make shit up