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There have been articles about how "data is the new oil" for a couple of decades now, with the first reference I could find being from British mathematician Clive Humby in 2006 [0]. The fact that it rings even more true in the age of LLMs is simply just another transformation of the fundamental data underneath.
I am specifically referring to the phrase I quoted, not some more abstract sentiment.
Bard's probably just a middle man here.
The response: "I'm a text-based AI, and that is outside of my capabilities."
For me it returns a seemingly accurate answer [1], albeit missing his involvement with Twitter/X. But LLMs are intrinsically stochastic, so YMMV.
Another interesting line of inquiry (potentially revealing some biases) is to ask it whether someone is a supervillain. For certain people it will rule it out entirely, and for others it will tend to entertain the possibility by outlining reasons why they might be a supervillain, and adding something like "it is impossible to say definitively whether he is a supervillain" at the end.
I really think they need to train on the wider dataset, then fine tune with some training on a machine specific dataset, then the model can reference data sources rather than have them baked in.
A lot of the general purposeness but also sometimes says weird things and makes specific references is pretty much down to this I reckon...it's trained on globs of human data from people in all walks of life with every kind of opinion there is so it doesn't really result in a clean model.
If you ask the same questions to ChatGPT you tend to get much more refined answers.
Ironing out is definitely the part where they're tweaking the model after the fact, but I wonder if we don't still need to separate language from culture.
It could help really, since we want a model that can speak a language, then apply a local culture on top. There's already been all sorts of issues arise with the current way of doing it, the Internet is very America/English centric and therefore most models are the same.