I think this is a really interesting question since it seems like it should neatly split the “LLMs are just next token predictors” crows from the “LLMs actually display understanding” crowd.
If in order to make statements about chairs and tables an LLM builds a model of what a chair and a table actually are, and to answer questions about fgeyjajic and chandybsnx it builds a model of what they are, it should be able to notice that these models correspond. At the very least it should be surprising if it can’t do that.
If it can’t generalize beyond stuff in the training set, and doesn’t display any ‘true’ intelligence, then it would be surprising if it can translate between two languages where it’s never seen any examples of translation before.
I think this is a really interesting question since it seems like it should neatly split the “LLMs are just next token predictors” crows from the “LLMs actually display understanding” crowd.
If in order to make statements about chairs and tables an LLM builds a model of what a chair and a table actually are, and to answer questions about fgeyjajic and chandybsnx it builds a model of what they are, it should be able to notice that these models correspond. At the very least it should be surprising if it can’t do that.
If it can’t generalize beyond stuff in the training set, and doesn’t display any ‘true’ intelligence, then it would be surprising if it can translate between two languages where it’s never seen any examples of translation before.