Simultaneously, if you hire human translators, you are likely to get machine translations. Maybe not often or overtly, but the translation industry has not been healthy for a while.

To a large extent both "hallucinations" and "plagiarism" can be addressed with the same training method: source-aware training.

https://arxiv.org/abs/2404.01019

At the frontier of science we have speculations, which until proper measurements become possible, are unknown to be true or false (or even unknown to be equivalent with other speculations etc. regardless of their being true or false, or truer or falser). Once settled we may call earlier but wrong speculations as "reasonable wrong guesses". In science it is important that these guesses or suspicions are communicated as it drives the design of future experiments.

I argue that more important that "eliminating hallucinations" is tracing the reason it is or was believed by some.

With source-aware training we can ask an LLM to give answers to a question (which may contradict each other), but to provide the training-source(s) justifying emission of each answer, instead of bluff it could emit multiple interpretations and go like:

> answer A: according to school of thought A the answer is that ... examples of authors and places in my training set are: author+title a1, a2, a3, ...

> answer B: according to author B: the answer to this question is ... which can be seen in articles b1, b2

> answer ...: ...

> answer F: although I can't find a single document explaining this, when I collate the observation x in x1, x2, x3; observation y in y1,y2, ... , observation z in z1, z2, ... then I conclude the following: ...

so it is clear which statements are sourced where, and which deductions are proper to the LLM.

Obviously few to none of the high profile LLM providers will do this any time soon, because when jurisdictions learn this is possible they will demand all models to be trained source-aware, so that they can remunerate the authors in their jurisdiction (and levy taxes on their income). What fraction of the income will then go to authors and what fraction to the LLM providers? If any jurisdiction would be first to enforce this, it would probably be the EU, but they don't do it yet. If models are trained in a different jurisdiction than the one levying taxes the academic in-group citation game will be extended to LLMs: a US LLM will have incentive to only cite US sources when multiple are available, and a EU trained LLM will prefer to selectively cite european sources, etc.

The plagiarism by the models is only part of it. Perhaps it's in such small pieces that it becomes difficult to care. I'm not convinced.

The larger, and I'd argue more problematic, plagiarism is when people take this composite output of LLMs and pass it off as their own.

I disagree that the majority of it is anti-LLM ranting, there are several subtle points here that are grounded in realism. You should read on past the first bit if you're judging mainly from the initial (admittedly naive) first few paragraphs.

What's wrong about the statement? The black box algorithm might have been generated by machine learning, but it's still a computer program in the end.

> I can't imagine why someone would want to openly advertise that they're so closed minded.

It's not being closed-minded. It's not wanting to get sea-lioned to death by obnoxious people.

> I can't imagine why someone would want to openly advertise that they're so closed minded.

I would say the exact same about you, rejecting an absolutely accurate and factual statement like that as closed minded strikes me as the same as the people who insist that medical science is closed minded about crystals and magnets.

I can't imagine why someone would want to openly advertise they think LLMs are actual intelligence, unless they were in a position to benefit financially from the LLM hype train of course.

It was actually much less anti LLM than I was expecting from the beginning.

But I agree that it is self limiting to not bother to consider the ways that LLM inference and human thinking might be similar (or not).

To me, they seem do a pretty reasonable emulation of single- threaded thinking.

> I can't imagine why someone would want to openly advertise that they're so closed minded.

Because humans often anthropomorphize completely inert things? E.g. a coffee machine or a bomb disposal robot.

So far whatever behavior LLMs have shown is basically fueled by Sci-Fi stories of how a robot should behave under such and such.

I dunno, I enjoyed reading about how the author personally feels about the act of working with them more then the whole "is this moral" part.

I don't know, this one is a little novel. I've never seen the developer of a Buddhist meditation app discuss whether to use LLMs with a paragraph like:

> Pariyatti’s nonprofit mission, it should be noted, specifically incorporates a strict code of ethics, or sīla: not to kill, not to steal, not to engage in sexual misconduct, not to lie, and not to take intoxicants.

Not a whole lot of Pali in most LLM editorials.

> The plagiarism argument is thin and feels biased

are you being serious with this one

Too bad. You missed some interesting stuff. And I say that as someone who sees some of this very differently than the author.

Announcing that one line of the piece made you mad without providing any other thought is not very constructive.

> That view of humans - and LLMs - ignores the fact that when you combine large numbers of simple building blocks, you can get completely novel behavior.

I can bang smooth rocks to get sharper rocks; that doesn't make sharper rocks more intelligent. Makes them sharper, though.

Which is to say, novel behavior != intelligence.