They can if they've been post trained on what they know and don't know. The LLM can first been given questions to test its knowledge and if the model returns a wrong answer, it can be given a new training example with an "I don't know" response.
“Hallucination” is seeing/saying something that a sober person clearly knows is not supposed to be there, e.g. “The Vice President under Nixon was Oscar the Grouch.”
Harry Frankfurt defines “bullshitting” as lying to persuade without regard to the truth. (A certain current US president does this profusely and masterfully.)
“Confabulation” is filling the unknown parts of a statement or story with bits that sound as-if they could be true, i.e. they make sense within the context, but are not actually true. People with dementia (e.g. a certain previous US president) will do this unintentionally. Whereas the bullshitter generally knows their bullshit to be false and is intentionally deceiving out of self-interest, confabulation (like hallucination) can simply be the consequence of impaired mental capacity.
> Frankfurt understands bullshit to be characterized not by an intent to deceive but instead by a reckless disregard for the truth.
That is different than defining "bullshitting" as lying. I agree that "confabulation" could otherwise be more accurate. But with previous definition they are kinda synonyms? And "reckless disregard for the truth" may hit closer.
The paper has more direct quotes about the term.
You're right. It's "intent to persuade with a reckless disregard for the truth." But even by this definition, LLMs are not (as far as we know) trying to persuade us of anything, beyond the extent that persuasion is a natural/structural feature of all language.
Claude 4's system prompt was published and contains:
"Claude’s reliable knowledge cutoff date - the date past which it cannot answer questions reliably - is the end of January 2025. It answers all questions the way a highly informed individual in January 2025 would if they were talking to someone from {{currentDateTime}}, "
I thought best guesses were that Claude's system prompt ran to tens of thousands of tokens, with figures like 30,000 tokens being bandied about.
But the documentation page linked here doesn't bear that out. In fact the Claude 3.7 system prompt on this page clocks in at significantly less than 4,000 tokens.
Yup. Either the system prompt includes a date it can parrot, or it doesn't and the LLM will just hallucinate one as needed. Looks like it's the latter case here.
Technically they don’t, but OpenAI must be injecting the current date and time into the system prompt, and Gemini just does a web search for the time when asked.
the point is you can't ask a model what's his training cut off date and expect a reliable answer from the weights itself.
closer you could do is have a bench with -timed- questions that could only know if had been trained for that, and you'd had to deal with hallucinations vs correctness etc
just not what llm's are made for, RAG solves this tho
What would the benefits be of actual time concepts being trained into the weights? Isn’t just tokenizing the dates and including those as normal enough to yield benefits?
E.g. it probably has a pretty good understanding between “second world war” and the time period it lasted. Or are you talking about the relation between “current wall clock time” and questions being asked?
what i mean i guess is llms can -reason- linguistically about time manipulating language, but can't really experience it. a bit like physics. thats why they do bad on exercises/questions about physics/logic that their training corpus might not have seen.
Different teams who work backend/frontend surely, and the people experimenting on the prompts for whatever reason wanna go through the frontend pipeline.
> Which version of tailwind css do you know?
> I have knowledge of Tailwind CSS up to version 3.4, which was the latest stable version as of my knowledge cutoff in January 2025.