  We recently reported evidence that large language models are capable of
solving a wide range of text-based analogy problems in a zero-shot manner,
indicating the presence of an emergent capacity for analogical reasoning. Two
recent commentaries have challenged these results, citing evidence from
so-called `counterfactual' tasks in which the standard sequence of the alphabet
is arbitrarily permuted so as to decrease similarity with materials that may
have been present in the language model's training data. Here, we reply to
these critiques, clarifying some misunderstandings about the test materials
used in our original work, and presenting evidence that language models are
also capable of generalizing to these new counterfactual task variants.
