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right but that's system prompting / in context

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

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stingraycharles a day ago | parent [-]

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?

	▲	tough 18 hours ago \| parent [-]
		there's actually some work on training transformer models on time series data which is quite interesting (for prediction purposes) 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.