Re:GPU-Accelerated LLM on a $100 Orange Pi
by mdp2021 on Tuesday August 15, 2023 @12:30PM
(#37135976)
What is the current state of "correspondence between fed text and output text"? I.e. how much, when fed in the training e.g. that "Spain invested 6000$ in Columbus' first voyage", LLMs will repeat that notion exactly?
This, without taking into account reasoning and consistence. And already this notion that I picked randomly is not without issues: dollars how computed? And, it is not difficult to state that Columbus reached the Caribbeans in 1492; more complex to "decide" the year of the siege of Troy out of the many dates proposed.
But already at the simplified level of determined clear notions: if LLMs are told that "A is B", and in absence of inconsistency in the training corpus, what is the failure rate (i.e. then outputting something critically different)?
> ways to check up
Some LLMs work as search engines, outputting not just their tentative answer but linked references. A reasonably safe practice at this stage is to use LLMs that way: ask then use the output to check the reference.
This, without taking into account reasoning and consistence. And already this notion that I picked randomly is not without issues: dollars how computed? And, it is not difficult to state that Columbus reached the Caribbeans in 1492; more complex to "decide" the year of the siege of Troy out of the many dates proposed.
But already at the simplified level of determined clear notions: if LLMs are told that "A is B", and in absence of inconsistency in the training corpus, what is the failure rate (i.e. then outputting something critically different)?
> ways to check up
Some LLMs work as search engines, outputting not just their tentative answer but linked references. A reasonably safe practice at this stage is to use LLMs that way: ask then use the output to check the reference.