This only applies to intra-distribution "generalisation", which is not the meaning of the term we've come to associate with science. Here generalisation means across all environments (ie., something generalises if its
valid and
reliable where valid = measures property, and reliable = under causal permutation to the environment).
Since an LLM does not change in response to the change in meaning of terms (eg., consider the change to "the war in ukraine" over the last 10 years) -- it isn't reliable in the scientific sense. Explaining why it isnt valid would take much longer, but its not valid either.
In any case: the notion of 'generalisation' used in ML just means we assume there is a single stationary distribution of words, and we want to randomly sample from that distribution without bias to oversampling from points identical to the data.
Not least that this assumption is false (there is no stationary distribution), it is also irrelevant to generalisation in traditional sense. Since whether we are biased towards the data or not isn't what we're interested in. We want output to be valid (the system to use words to mean what they mean) and to be reliable (to do so across all environments in which they mean something).
This does not follow from, nor is it even related to, this ML sense of generalisation. Indeed, if LLMs generalised in this sense, they would be very bad at usefully generalising -- since the assumptions here are false.
Since an LLM does not change in response to the change in meaning of terms (eg., consider the change to "the war in ukraine" over the last 10 years) -- it isn't reliable in the scientific sense. Explaining why it isnt valid would take much longer, but its not valid either.
In any case: the notion of 'generalisation' used in ML just means we assume there is a single stationary distribution of words, and we want to randomly sample from that distribution without bias to oversampling from points identical to the data.
Not least that this assumption is false (there is no stationary distribution), it is also irrelevant to generalisation in traditional sense. Since whether we are biased towards the data or not isn't what we're interested in. We want output to be valid (the system to use words to mean what they mean) and to be reliable (to do so across all environments in which they mean something).
This does not follow from, nor is it even related to, this ML sense of generalisation. Indeed, if LLMs generalised in this sense, they would be very bad at usefully generalising -- since the assumptions here are false.