Let's take the work of Raymond Carver as just one example. He would type drafts which would go through repeated iteration with a massive amount of hand-written markup, revision and excision by his editor.

To really recreate his writing style, you would need the notes he started with for himself, the drafts that never even made it to his editor, the drafts that did make to the editor, all the edits made, and the final product, all properly sequenced and encoded as data.

In theory, one could munge this data and train an LLM and it would probably get significantly better at writing terse prose where there are actually coherent, deep things going on in the underlying story (more generally, this is complicated by the fact that many authors intentionally destroy notes so their work can stand on its own--and this gives them another reason to do so). But until that's done, you're going to get LLMs replicating style without the deep cohesion that makes such writing rewarding to read.

Professional writer here. On our longer work, we go through multiple iterations, with lots of teardowns and recalibrations based on feedback from early, private readers, professional editors, pop culture -- and who knows. You won't find very clear explanations of how this happens, even in writers' attempts to explain their craft. We don't systematize it, and unless we keep detailed in-process logs (doubtful), we can't even reconstruct it.

It's certainly possible to mimic many aspects of a notable writer's published style. ("Bad Hemingway" contests have been a jokey delight for decades.) But on the sliding scale of ingenious-to-obnoxious uses for AI, this Grammarly/Superhuman idea feels uniquely misguided.

The distinction being made is the difference between intellectual knowledge and experience, not originality.

Imagine a interviewing a particularly diligent new grad. They've memorized every textbook and best practices book they can find. Will that alone make them a senior+ developer, or do they need a few years learning all the ways reality is more complicated than the curriculum?

LLMs aren't even to that level yet.

> because a great deal of writers have described the process of writing in detail

And that's often inaccurate - just as much as asking startup founders how they came to be.

Part of it is forgot, part of it is don't know how to describe it and part of it is don't want to tell you so.

You will produce output that emulates the patters of Shakespeare's works, but you won't arrive at them by the same process Shakespeare did. You are subject to similar limitations as the llm in this case, just to a lesser degree (you share some 'human experience' with the author, and might be able to reason about their though process from biographies and such)

As another example, I can write a story about hobbits and elves in a LotR world with a style that approximates Tolkien. But it won't be colored by my first-hand WW1 experiences, and won't be written with the intention of creating a world that gives my conlangs cultural context, or the intention of making a bedtime story for my kids. I will never be able to write what Tolkien would have written because I'm not Tolkien, and do not see the world as Tolkien saw it. I don't even like designing languages

>> i again don't get what the point is?

The point is that you dont become Jimi Hendrix or Eric Clapton even if you spend 20 years playing on a cover band. You can play the style, sound like but you wont create their next album.

Not being Jimi Hendrix or Eric Clapton is the context you are missing. LLMs are Cover Bands...

You can understand his biography and analyses about how shakespeare might have written. You can apply this knowledge to modify your writing process.

The LLM does not model text at this meta-level. It can only use those texts as examples, it cannot apply what is written there to it's generation process.

This is the plot of a short story of Borges’ called “Pierre Menard, the Author of Don Quixote.”

> And LLMs do have understanding.

They absolutely do not. If you "ask it how it came up with the process in natural language" with some input, it will produce an output that follows, because of the statistics encoded in the model. That output may or may not be helpful, but it is likely to be stylistically plausible. An LLM does not think or understand; it is merely a statistical model (that's what the M stands for!)