Today’s language models are more sophisticated than ever, but they still struggle with the concept of negation. That’s unlikely to change anytime soon.
this isn’t necessarily true. patterns in data aren’t by nature proof of an underlying system of logic. if you run the line-fitting machine on any kind of data, its going to output a line. considering just how much data is encoded into these transformers, i don’t think we can conclusively say that it has a underlying conception of how language works, much less an understanding of the concepts that language represents. it could really just be using the vast quantities of data it has to output approximately correct statements. there’s absolutely structure there, but it doesn’t have to have the kind of structured understanding humans have about language to produce language, in the same way a less sophisticated machine learning model doesn’t have to know what kind of data its fitting a line to to make a line.
this isn’t necessarily true. patterns in data aren’t by nature proof of an underlying system of logic. if you run the line-fitting machine on any kind of data, its going to output a line. considering just how much data is encoded into these transformers, i don’t think we can conclusively say that it has a underlying conception of how language works, much less an understanding of the concepts that language represents. it could really just be using the vast quantities of data it has to output approximately correct statements. there’s absolutely structure there, but it doesn’t have to have the kind of structured understanding humans have about language to produce language, in the same way a less sophisticated machine learning model doesn’t have to know what kind of data its fitting a line to to make a line.