> 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.
LLMs can not reliably tell whether they know or don't know something. If they did, we would not have to deal with hallucinations.
“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.
E.g. from the paper ChatGPT is bullshit [1],
> 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.
[1] https://link.springer.com/article/10.1007/s10676-024-09775-5
That's.. how many questions? Maybe if one model generates all possible questions then
"Who is president?" gives a "April 2024" date.
a model learns words or tokens more pedantically but has no sense of time nor cant track dates
not really -trained- into the weights.
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
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?
see google TimesFM: https://github.com/google-research/timesfm
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.
sometimes its interesting to peek up under the network tab on dev tools
"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}}, "
https://docs.anthropic.com/en/release-notes/system-prompts#m...
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.