that's fair and you have highlighted a good limitation. but we do this all the time - we try to understand the author, learn from them and mimic them and we succeed to good extent.

that's why we have really good fake van gogh's for which a person can't tell the difference.

of course you can't do the same as the original person but you get close enough many times and as humans we do this frequently.

in the context of this post i think it is for sure possible to mimic a dead author and give steps to achieve writing that would sound like them using an LLM - just like a human.

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Peritract 8 hours ago | parent [-]

You're still confusing "has a result that looks the same" and "uses the same process"; these are different things.

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simianwords 6 hours ago | parent [-]

Why do you say it has a different process? When I ask it to do integrals it uses the same process as me

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Peritract 5 hours ago | parent | next [-]

Not everything works like integrals. Some things don't have a standard process that everyone follows the same way.

Editing is one of these things. There can be lots of different processes, informed by lots of different things, and getting similar output is no guarantee of a similar process.

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simianwords 3 hours ago | parent | next [-]

I don’t see why editing is any different. If a human can learn it why not an llm

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esafak 3 hours ago | parent | prev [-]

The process is irrelevant if the output is the same, because we never observe the process. I assume you are arguing that the outputs are not guaranteed to be the same unless you reproduce the process.

If we are talking about human artifacts, you never have reproducibility. The same person will behave differently from one moment to the next, one environment to another. But I assume you will call that natural variation. Can you say that models can't approximate the artifacts within that natural variation?

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Rohansi 2 hours ago | parent | next [-]

It's relevant for data it hasn't been trained on. LLMs are trained to be all-knowing which is great as a utility but that does not come close to capturing an individual.

If I trained (or, more likely, fine-tuned) an LLM to generate code like what's found in an individual's GitHub repositories, could you comfortably say it writes code the same way as that individual? Sure, it will capture style and conventions, but what about our limitations? What do you think happens if you fine-tune a model to write code like a frontend developer and ask it to write a simple operating system kernel? It's realistically not in their (individual) data but the response still depends on the individual's thought process.

	▲	simianwords 8 minutes ago \| parent \| next [-]
		>If I trained (or, more likely, fine-tuned) an LLM to generate code like what's found in an individual's GitHub repositories, could you comfortably say it writes code the same way as that individual? Sure, it will capture style and conventions, but what about our limitations? What do you think happens if you fine-tune a model to write code like a frontend developer and ask it to write a simple operating system kernel? It's realistically not in their (individual) data but the response still depends on the individual's thought process. Look, I don't think you understand how LLM's work. Its not about fine tuning. Its about generalised reasoning. The key word is "generalised" which can only happen if it has been trained on literally everything. > It's relevant for data it hasn't been trained on LLM's absolutely can reason on and conceptualise on things it has not been trained on, because of the generalised reasoning ability.
	▲	esafak an hour ago \| parent \| prev [-]
		I don't know if LLMs are trained to imitate sources like that. I also don't know what would happen if you asked it to do something like someone who does not know how to do it. Would they refuse, make mistakes, or assume the person can learn? Humans can do all three, so barring more specific instructions any such response is reasonable.

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volkk an hour ago | parent | prev [-]

i think there's a lot to be said about the process as well, the motivations, the intuitions, life experiences, and seeing the world through a certain lens. this creates for more interesting writing even when you are inspired by a certain past author. if you simply want to be a stochastic parrot that replicates the style of hemingway, it's not that difficult, but you'll also _likely_ have an empty story and you can extend the same concept to music

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arkadiytehgraet an hour ago | parent | prev [-]

Even if the visualization of the integration process via steps typed out in the chat interface is the same as what you would have done on paper, the way the steps were obtained is likely very different for you and LLM. You recognized the integral's type and applied corresponding technique to solve it. LLM found the most likely continuation of tokens after your input among all the data it has been fed, and those tokens happen to be the typography for the integral steps. It is very unlikely are you doing the same, i.e. calculating probabilities of all the words you know and then choosing the one with the highest probability of being correct.

	▲	simianwords 26 minutes ago \| parent [-]
		> the way the steps were obtained is likely very different for you and LLM this is not true, any examples?