[esafak]

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?

[Rohansi]

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.

[esafak]

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.

[Rohansi]

> Humans can do all three, so barring more specific instructions any such response is reasonable.

Of course, but reasonable behavior across all humans is not the same as what one specific human would do. An individual, depending on the scenario, might stick to a specific choice because of their personality etc. which is not always explained, and heavily summarized if it is.

[simianwords]

>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.

[Rohansi]

> LLM's absolutely can reason on and conceptualise on things it has not been trained on, because of the generalised reasoning ability.

Yes, but how does that help it capture the nuances of an individual? It can try to infer but it will not have enough information to always be correct, where correctness is what the actual individual would do.

[volkk]

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