I have this idea that a tiny LM would be good at canonicalizing entered real estate addresses. We currently buy a data set and software from Experian, but it feels like something an LM might be very good at. There are lots of weirdnesses in address entry that regexes have a hard time with. We know the bulk of addresses a user might be entering, unless it's a totally new property, so we should be able to train it on that.
From my experience (2018), run LLM output through beam search over different choices of canonicalization of certain part of text. Even 3-gram models (yeah, 2018) fare better this way.