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.
Headlines should not say “scientists,” they should name the institution. (Harvard in this case.)
Headlines should not say “Harvard”, they should name the researchers. (Rachel Greene in this case.)
I don’t know why I had to write this.
Who’s Rachel Greene? But we all know Harvard and have an idea of their respectability. Name of the researcher if not well-known should be in the body instead.
Makes sense if you look at abliterated models. Once abliterated and retrained they seem to improve. Imo we are adding too much human bias by trying to guide the LLM. Censored models are good and need to be used in some situations, but shouldn’t the base be just data and only then finetune to desired output?
So is it saying essentially that in order to not output garbage, it needs to know first what garbage is?
Is it just me that things this seems like a no-brainer?
It almosr draws parallels to many societal issues. Knowledge is power.
People tend towards intolerance and hatred when they dont understand the thing they are angry at. The more they know the better they behave.
No it’s more of a technical discussion. Many people might believe that in order to avoid toxicity, you just train a model on “good” non-toxic data and then apply toxicity removal techniques to address emergent toxicity that the model might spit out. This paper is saying they found it more effective to train the model on a small percentage of “bad” toxic data on purpose, then apply those same toxicity removal techniques. For some reason, that actually generated less total toxicity. It’s an interesting result. A wild guess on my part, but I’m thinking training the model with toxic content “sharpened” the toxicity when it was generated, making it easier for those removal tools to identify it.
Give the AI model the gift of culture and class. No suprise it behaves better
Sophistication my good sir.
I envision a Gemini powered bot that cracks captcha and posts “woke” replies on 4chan. If you’re an antivaxxer, antisemite, nazi, racist, sionist, or otherwise, it will debate you. It will not get tired. It will not get mad. It will maintain a sense of decorum indefinitely and it will never ever stop. If some far right extremist decides to do the same, it will have the advantage that academia is left leaning, meaning the model can cite widely recognized studies.
Dead internet theory and so on, but I’ll gladly completely and utterly destroy the internet if it means the filth dies with it.
There’s little evidence that debate changes people’s ideas.
It’s not about changing their ideas. The target is the audience.
yeah, this only works in scientific fields
it will have the advantage that academia is left leaning, meaning the model can cite widely recognized studies.
I was looking for the person saying a particular quote yesterday.
I asked 3 times the same question and I got 3 different people.
The funny part us I had the quote wrong.
Bullshit all the way down.
because 4chan users write original content. that is fed into the next best stupid platform and so on until it ends on tiktok or whatever.
if you have nothing to say you use meta/tiktok. no relevabt content has ever been there first. copies and derivates, yes…
so soonish AI will flood 4chan so ai scrapers get polluted aswell…and then it is dead.
It has nothing to do with that, and much more to do with people on 4chan being willing to call each other out. Without toxic behavior you can’t have examples on how to deal with toxic behavior.
can we stop referring to llm’s as if they’re capable of thought? they don’t make decisions; their programming just responds to patterns.
10% 4chan
why didn’t they just say 0.4chan and be done with it?
Don’t have gold, but please get out anyways.
Best comment I’ve read this week
Underrated comment.
Seems pretty rated to me
This is not surprising if you’ve studied anything on machine learning or even just basic statistics. Consider if you are trying to find out the optimal amount of a thickener to add to a paint formulation to get it to flow the amount you want. If you add it at 5%, then 5.1%, then 5.2%, it will he hard to see how much of the difference between those batches is due to randomness or measurement uncertainty than if you see what it does at 0%, then 25% then 50%. This is a principle called Design of Experiments (DoE) in traditional statistics, and a similar effect happens when you are training machine learning models- datapoints far outside the norm increase the ability of the model to predict within the entire model space (there is some nuance here, because they can become over-represented if care isn’t taken). In this case, 4chan shows the edges of the English language and human psychology, like adding 0% or 50% of the paint additives rather than staying around 5%.
At least that’s my theory. I haven’t read the paper but plan to read it tonight when I have time. At first glance I’m not surprised. When I’ve worked with industrial ML applications, processes that have a lot of problems produce better training data than well controlled processes, and I have read papers on this subject where people have improved performance of their models by introducing (controlled) randomness into their control setpoints to get more training data outside of the tight control regime.
I know everyone on Lemmy hates LLMs, but this is really interesting
I like LLMs. I’m aware of their limitations, and I use them daily.
I do hate LLMs (or how they’re marketed/hyped/used) and I concur that this is very interesting science
I appreciate your reasoned and measured reply, friend!
I dislike that people are relying on them to do all their thinking for them while also being incredibly interested in the tech behind them.
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.
The problem is that before LLMs, they had to actually put forward some effort to produce content on the internet, which at least kept the amount of thoughtless content down somewhat. Now the barrier to entry is practically zero, all while thieving people’s hard work without compensation and burning ridiculous amounts of resources to do so.
It is super interesting tech though.
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.
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
No, we were juat eating tide pods. Dumb gonna do what dumb gonna do. The only real issue with llms is that their training data is stolen, and that theyre currently not that useful due to hallucinations and lacking logical reasoning.
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.
Sometimes things aren’t obvious unless you already have the knowledge. If an AI tool tells a young person cleaning their first apartment to combine household cleaners, are they stupid for doing so? Maybe. They may not have the experience to know. Stupid people deserve to live free from harm too, and we’re all a little stupid.
There’s a balance to be struck.
Strongly disagree. Survival of the fittest based eugenics is not acceptable. Stupid people don’t deserve to suffer.
What do you all mean by “thinking”? Forming opinions or solving problems?
Both.
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”.
That sounds like a terrible company, NGL. I’m sorry there aren’t other options for you.
I don’t dislike LLMs, I dislike people who treat them as anything more than an advanced search engine and stupidly give them all their confidential data. Seen it happen too much at work.
Yep. My work is very strict about security except for when it comes to LLMs, and then suddenly they’re surprisingly lax about it. It’s a bit concerning actually.
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.
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.
That’s why I said “as standalone things.” As a computing curiosity, they’re amazing. No language processing application like this existed 30 years ago when I was a kid. You could also see “talking computers” speaking naturally, pretending or not, on movies and TV shows.
Those numbers are baseless exaggerations. There are plenty of tasks which they solve perfectly, today. It’s just that a bunch of dicks operate them, and the cost of operating them are way too high.
Also:
- environmental impact of AI
- unethical acquisition of training data
- dichotomy of how conservative politics treat AI company and private copyright law
- “undress AI” and deepfakes
It’s not that they’re not useful, that’s just nonsense.
There are plenty of tasks which they solve perfectly, today.
Name a single task you would trust an LLM on solving for you that you feel confident would be correct without checking the output. Because that is my definition of perfectly and AI falls very, very far short of that.
“Hey AI, write me a random poem about taladar.”
i used it when i traveled to japan to ask it for english->japanese translations. it gave back results for multiple contexts, politeness levels, and broke down each sentence into its parts. my native speaker friends validated a few responses.
if youre going to be pedantic about “perfect” then nothing, not even a human, is going to live up.
willful ignorance about the things ai can be good at today is not going to do any favors for your fight against ai in the future. know your enemy and all that.
Who says you can’t check their outputs? It’s much faster to e. g. read a generated text than to write everything yourself. Same applies to translations, they’ve been excellent for quite a while now.
Business communication can be handled effortlessly by AI. Of course you read the result before you send it out, but that takes an order of a magnitude less time than formulating and typing all those meaningless sentences.
And honestly, that’s a perfect use case for AI. I wouldn’t compose a love letter to my family using AI, but a pamphlet, feature description, sales pitch, any bullshit presentation deck? You bet AI excels at those.
Same applies to content summaries that help augment search indices. Finding a large number of content candidates (e. g. videos) and have AI summarize the contents of said videos to narrow down the search is helpful and works today.
I’m not looking for AGI. I’m looking for tools to make my life easier, but in an ethical manner that doesn’t advance the destruction of the planet at an exponential rate, just for some tech bro to jerk it and buy another yacht.
You can make a generic fill in the blanks for all of those like I do and just change the key terminology for each scenario. LLMs are competing with search and replace?
I think this may be a skill issue on your part.
That’s a bit too dismissive. I’ve had a lot of interesting chats with LLMs that led me to find out what I didn’t understand about something. As an example I’m reading a book explaining some practices of Structured Concurrency in Swift and many times I asked ChatGPT is the author is correct about some phrasing that seemed wrong to me. And ChatGPT was able to explain why that was right in that context.
They are essentially a fun toy for most people, and an ok tool for people with the patience and training to get useful output from them. And they cost an insane amount of money to train and an insane amount of power to run.
Not to mention the other cost of training them, the human emotional cost. And the human cost of running them.
It just costs so much of a variety of things, for an output that has barely made anything better. Maybe they might get “better” in the future, and have to get through this stage to get there, but I’ve also seen a lot of people saying they appear to be starting to plateau… maybe a temporary plateau, but if so, how temporary? Could we just drop it for 10 years and start back up when they won’t be as inefficient? Maybe a law that they have to pay for everything they feed it, would effectively cause them to only emerge at a time when they are actually feasible.
People who track performance (like METR, a nonprofit) indicate that progress is, if anything, speeding up. Most people’s use case is so simple they can’t detect the difference. However for cases like complex problem solving, agentic tasks, etc you can in fact see significant progress happening. This should be concerning if you think the world isn’t ready for labor displaced by LLMs.
I wish they would tone down the crusade. This is some of the most interesting technology to come out in decades.
It’s extremely useful for many things, if you know how to use it, and it’s annoying and useless for many others, which is what they fixate on and keep-jerk react to
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
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) 😅
And I wish they would tone down the hype. Maybe we can meet in the middle?
Well, I do wish they would promote the actual use and limitations of AI and stop making up crap and overselling the use cases. I use ChatGPT at work all the time as a start for research, but if I took any of it as being reliable info to run with I would be in grave trouble. It is a great tool that has saved me much time because I know how far to trust it and how to use it. The progress is very impressive as I’ve been using AI art services for years, and the difference between the random blobs from back then and the great stuff it can generate now is pretty stark. Same thing with the LLMs. I’ve been using ChatGPT since it showed up and it has improved greatly since then. Before all this I talked to people who were using AI training on various picture recognition projects where getting data from other sensors was not practical. … Overall AI is pretty exciting, but the non-stop hype and hate headlines is doing nobody any favors.
I’m cool with it. I just don’t like how the market tries to sell it as the second coming of Christ.
“Don’t believe that marketing department“ is one of those things everybody needs to learn at some point in their life.
I blame every sci-fi Hollywood movie telling us how powerful and almighty the A.I is. How it’s going to be the magic pill that entirely destroys or saves humanity by itself.
Now we have an entire generation believing this crap.
I mean, it still could be. But LLMs are not that AGI we’re expecting.
The difficult question about AGI destroying humanity is deciding whether to be afraid of that option or to cheer it on and LLM enthusiasts are certainly among the people heavily pushing me towards the ‘cheer it on’ option.
You can blame Hollywood for a lot of things, including this, but sci-fi authors have been doing it for longer. That’s where Hollywood took those stories from in the first place.
This is the same market that tried to add blockchain to everything when that first became well-known.
Some of the biggest forces in the market are extraordinarily stupid people trying to ride every buzzword that comes along.
Some of the biggest forces in the market are extraordinarily stupid people trying to ride every buzzword that comes along.
I think the biggest forces sell the fantasy to smaller forces. This way they can capitalize on the smaller forces believing the hype.
I like LLMs. Instead of making a racket, I just use them, which may make it seem like everyone on Lemmy hates LLMs.
Being a teacher In academia is what makes me hate them tbh
I love how everyone tries to jump on your comment after being called out and act like they don’t absolutely hate every stitch of it. But even in their excuses you can see the lies.
Those are actually some very good results. Funny situation, if the copyright companies win the AI legislative war, 4chan is going to get twice as much as reddit did for the data at the minimum.
It’s also interesting the model gets worse faster if it has to untrain the toxic data so to speak.
So basically… by being familiar with 4chan the model knows better what not to do?
Yup. Sucks for everyone having fun jailbreaking them. It is going to get much harder.
Based and hopepilled
Boy, I don’t even know if I wish that much 4chan on a LLM.
It is truly a bizzare world, I went there first to be edgy as an early teen and seeing boobs is fun, then I saw a dude live post his murder of a woman he liked while everyone called her names.
It makes a great case for moderation if not banning the internet.
When the AI only trained on 4chan dropping.
It needs to be fake and gay
That exists, its called GPT4chan, and it went exactly like you’d expect.
Fake and Bi
Interesting - I can sort of intuit why it might help. Feeding the model bad data and instructing training it to identify it as such would be advantageous compared to being entirely unaware of it.
Yeah, it’s like me never having alcohol before and walking into a frat party as a freshman. Sometimes it’s better to come prepared.
