Maybe in one shot.
replyIn theory I would expect them to be able to ingest the corpus of the new yorker and turn it into a template with sub-templates, and then be able to rehydrate those templates.
The harder part seems to be synthesizing new connection from two adjacent ideas. They like to take x and y and create x+y instead of x+y+z.
Most of the good major models are already very capable of changing their writing style.
replyJust give them the right writing prompt. "You are a writer for the Economist, you need to write in the house style, following the house style rules, writing for print, with no emoji .." etc etc.
The large models have already ingested plenty of New Yorker, NYT, The Times, FT, The Economist etc articles, you just need to get them away from their system prompt quirks.
No, the failure is the human written prompt
replyYou know, after a while this excuse is not valid anymore.
replySomeone here mentioned a whole ago that the labs deliberately haven't tried to train these characteristics out of their models, because leaving them in makes it easier to identify, and therefore exclude, LLM-generated text from their training corpus.
replyBut it's odd that these characteristics are the same across models from different labs. I find it hard to believe that researchers across competing companies are coordinating on something like that.
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