Just because something makes sense intuitively to one person, that doesn't mean it makes sense scientifically.

They're probably not testing anything further because they can't even define their terms.

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In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.

This is a "guns don't kill people - people kill people" kind of scenario.

As a standalone thing, LLMs are awesome.

What sucks is greedy people using them for the wrong reasons.

It's like robots. Playing with robots are awesome. Firing 1,000 people and replacing them with robots - and not sharing the benefits with the community sucks.

10% 4chan

why didn't they just say 0.4chan and be done with it?

I mean, it still could be. But LLMs are not that AGI we’re expecting.

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In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.

My gf's employer was going into administration last month. AI was surprisingly competent in determining where to seek advice and had a decent understanding of what to expect and how to approach things such as not getting paid on time (which happened last week).

Of course, we double and triple checked any information given to us with the relevant bodies, but it provided a little relief to go into something so chilling not being completely clueless.

AI has its use, but you have to know how to extract the information you need.

It's stupid the way people are using it for therapy. Like, by all means ask it if it knows any organisations which can help you, then look those up, but don't tell it a load of personal information about your relationship, because the reply will be something akin to the advice you see on r/relationships (which is probably where it scraped its data from)

As a standalone thing, LLMs are awesome.

They really aren't though and that is half the problem. Everyone pretends they are awesome when the results are unusable garbage 80% of the time which makes them unusable for 99% of practical applications.

This is true, but we don’t need people putting glue on their pizza. These people used to have a person to ask now they’ll be asking Sam Altman

I’ve said this a few times in a different way and I always get downvoted. The fact is that the people who will use the LLMs to think for them, were not gonna think a lot in the first place.

bad data

Can you define this? The authors/grifters call it "toxic data" but never define that either.

bad data

Can you define this? The authors/grifters call it "toxic data" but never define that either.

Well I would make the argument that someone stupid enough to do such a thing kinda deserves whatever consequences their actions have. I find that people learn faster when actions have consequences instead of everything being babyproofed.

I recently realized it's a non-issue. The people doing this have already been looking for decades to find new ways to rot their minds. LLMs are just the latest in a long line of tools that help them tune out.

Well I would make the argument that someone stupid enough to do such a thing kinda deserves whatever consequences their actions have. I find that people learn faster when actions have consequences instead of everything being babyproofed.

Not when companies force them on you as well.

My current company forces me to use it and measures how many prompts I’m making as “productivity”.

As a standalone thing, LLMs are awesome.

They really aren't though and that is half the problem. Everyone pretends they are awesome when the results are unusable garbage 80% of the time which makes them unusable for 99% of practical applications.

Ask the machine to generate a script to ask the machine to generate a list of 100 prompts and query the machine with each prompt over the course of an 8 hour workday

• HTML.
• PDF.

In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.

It’s annoying that every middle manager is trying to become the hero of their company by pushing it inappropriately into every single field at the expense of productivity and jobs, while simultaneously the largest most powerful companies are slinging their SaaS solutions built on stolen data which are destroying communities of both the physical and hobby varieties and consuming more natural resources than all the fucking crypto scams of the last like 10 years

But yeah it’s neat I guess