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Comment by impossiblefork | Hacker News Reader

Yes, but we understand he what means.

We even have alternative meanings for bias within ML, such as for the bias added before non-linearities in many neural networks.

He obviously means censored LLMs, and I think his view is actually right, although I'm far from sure that these firms are in some kind of scheme to produce LLMs biased in this sense.

Uncensored, tunable LLMs under the full control of their users could scour the internet for propaganda, look for connections between people and organisations and just generally make the work of propagandists who don't have their reader's interests in mind more difficult.

I think we'll end up with that anyway but it's a reasonable fear that there'd be people trying to prevent us from getting there.

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There is literally no such thing as an unbiased text generator. No matter how you cut it there are an infinite pool of prompts that will need some sort of implicit value system at the heart of the answer. Any implicit bias just from selection of training data will be reflected back at the user.

> Uncensored, tunable LLMs under the full control of their users could scour the internet for propaganda, look for connections between people and organisations and just generally make the work of propagandists who don't have their reader's interests in mind more difficult.

Even this example, what sources do you trust that is or is not "in the readers best interest", what is propaganda or what is an implicit value in a society, when you tune an LLM does that just mean you're steering it to give answers that you like more?

Creating an unbiased LLM is as much of a fools errand as creating an unbiased news publication

There is the model that you end up fine-tuning, which produces reasonable continuations of almost anything in its training dataset, whether it is something any approves of or not.

>Even this example, what sources do you trust that is or is not "in the readers best interest", what is propaganda or what is an implicit value in a society, when you tune an LLM does that just mean you're steering it to give answers that you like more?

You tune the model yourself. You tune it to find the things you're looking for and which interest you.

>Creating an unbiased LLM is as much of a fools errand as creating an unbiased news publication

It's what you do before pretraining. You model human-written texts with metadata and context with the intent of actually modeling those texts, rather than excising something which isn't just causing the model to fail to learn other things.

It's like, asking "what's a cake, really". We can argue about lines etc., but everbody knows. An unbiased language model is a reasonable thing to want and it's not complicated to understand what it is.

Can you imagine unbiased courts, as an ideal? Somebody who just doesn't care about anything other than certain things? Just as such a thing can be imagined, so can you imagine someone who doesn't about reality and just wants to understand human texts.

I'm not sure if you are reading what I am saying by bias. Human language, events, decisions, only exists in the cultural and historical contexts around it.

> unbiased courts

You say this as something could ever exist. A court will always have a bias because it is humans with values and morals that make a decision. Think about the classic "would you steal bread to feed your family", or even the trolley problem, or as a very concrete example the recent overturning of Roe v Wade in America (keeping in mind that both sides of that discussion reveal an implicit bias based on your starting set of morals and values). Any question that involves a base set of values and morals will never have an unbiased answer.

But there are people who are peculiar and decide to only do one thing. Such a person can easily decide that the only thing that matters is interpreting the law as written.

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