Earlier quoted context omitted.
Like the earlier responses said, if it is retrieving information from an external data source, that would be a much easier way to update things. REALM ( https://arxiv.org/abs/2002.08909 ) comes to mind as an example of active work in this area. Updating a monolithic LLM seems like a harder problem at the moment, and probably would fall under the umbrella of "continual learning", though that sub-field would have their…
> Like the earlier responses said, if it is retrieving information from an external data source I was under the impression it went through like a multi-month long ultra expensive training process involving many GPUs on terabytes of data "snapshotted" point in time. Is it possible to do this at a smaller scale once a day at the end of the day with "all new content scraped from the Internet nightly"?
Catch is: consider these statements:
Donald Trump is the President.
Joe Biden is the President.
Barack Obama is the President.
George W Bush is the President.
George H. W. Bush is the President.
Bill Clinton is the President.
All of them were true at some point of time. How do you train a model to disambiguate these?