Sure, but the author of TFA is well versed in LLMs and so is addressing something different. Novelty isn't the same as creativity, especially when limited to generating based on a fixed repertoire of moves.
The term "deductive closure" has been used to describe what LLMs are capable of, and therefore what they are not capable of. They can generate novelty (e.g. new poem) by applying the rules they have learnt in novel ways, but are ultimately restricted by their fixed weights and what was present in the training data, as well as being biased to predict rather than learn (which they anyways can't!) and explore.
An LLM may do a superhuman job of applying what it "knows" to create solutions to novel goals (be that a math olympiad problem, or some type of "creative" output that has been requested, such as a poem), but is unlikely to create a whole new field of math that wasn't hinted at in the training data because it is biased to predict, and anyways doesn't have the ability to learn that would allow it to build a new theory from the ground up one step at a time. Note (for anyone who might claim otherwise) that "in-context learning" is really a misnomer - it's not about learning but rather about using data that is only present in-context rather than having been in the training set.
The term "deductive closure" has been used to describe what LLMs are capable of, and therefore what they are not capable of. They can generate novelty (e.g. new poem) by applying the rules they have learnt in novel ways, but are ultimately restricted by their fixed weights and what was present in the training data, as well as being biased to predict rather than learn (which they anyways can't!) and explore.
An LLM may do a superhuman job of applying what it "knows" to create solutions to novel goals (be that a math olympiad problem, or some type of "creative" output that has been requested, such as a poem), but is unlikely to create a whole new field of math that wasn't hinted at in the training data because it is biased to predict, and anyways doesn't have the ability to learn that would allow it to build a new theory from the ground up one step at a time. Note (for anyone who might claim otherwise) that "in-context learning" is really a misnomer - it's not about learning but rather about using data that is only present in-context rather than having been in the training set.