I seriously keep reading LLM as MLM
I mean…
The real money is from buying AI from me, in bulk, then reselling that AI to new vict… customers. Maybe they could white label your white label!
So you’re saying that thorn guy might be on to somthing?
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Lmao
So, like with Godwin’s law, the probability of a LLM being poisoned as it harvests enough data to become useful approaches 1.
I mean, if they didn’t piss in the pool, they’d have a lower chance of encountering piss. Godwin’s law is more benign and incidental. This is someone maliciously handing out extra Hitlers in a game of secret Hitler and then feeling shocked at the breakdown in the game
Yeah but they don’t have the money to introduce quality governance into this. So the brain trust of Reddit it is. Which explains why LLMs have gotten all weirdly socially combative too; like two neckbeards having at it—Google skill vs Google skill—is a rich source of A+++ knowledge and social behaviour.
If I’m creating a corpus for an LLM to consume, I feel like I would probably create some data source quality score and drop anything that makes my model worse.
Then you have to create a framework for evaluating the effect of the addition of each source into “positive” or “negative”. Good luck with that. They can’t even map input objects in the training data to their actual source correctly or consistently.
It’s absolutely possible, but pretty much anything that adds more overhead per each individual input in the training data is going to be too costly for any of them to try and pursue.
O(n) isn’t bad, but when your n is as absurdly big as the training corpuses these things use, that has big effects. And there’s no telling if it would actually only be an O(n) cost.
Yeah, after reading a bit into it. It seems like most of the work is up front, pre filtering and classifying before it hits the model, to your point the model training part is expensive…
I think broadly though, the idea that they are just including the kitchen sink into the models without any consideration of source quality isn’t true
I’m sure that’s true, but it is also noteworthy that any and every consideration that goes into the initial inclusion of the data before it is fed into the model introduces intended and unintended consequences on the training.
Furthermore, the proliferation of the LLMs themselves is putting negative pressure on survival of the places where all the good data is sourced from in the first place. When traffic to a place like stackoverflow is way down because everyone’s reading LLM answers (that the LLM training dataset got from stack overflow), there are less good conversations on stackoverflow to read. Some of these sources of training data may even be caused to cease to exist entirely.
As far as I know that’s generally what is often done, but it’s a surprisingly hard problem to solve ‘completely’ for two reasons:
-
The more obvious one - how do you define quality? When you’re working with the amount of data LLMs require as input and need to be checked for on output you’re going to have to automate these quality checks, and in one way or another it comes back around to some system having to define and judge against this score.
There’s many different benchmarks out there nowadays, but it’s still virtually impossible to just have ‘a’ quality score for such a complex task.
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Perhaps the less obvious one - you generally don’t want to ‘overfit’ your model to whatever quality scoring system you set up. If you get too close to it, your model typically won’t be generally useful anymore, rather just always outputting things which exactly satisfy the scoring principle, nothing else.
If it reaches a theoretical perfect score, it would just end up being a replication of the quality score itself.
like the LLM that was finding cancers and people were initially impressed but then they figured out the LLM had just correlated a DR’s name on the scan to a high likelihood of cancer. Once the complicating data point was removed, the LLM no longer performed impressively. Point #2 is very Goodhart’s law adjacent.
I never knew the name for this law, but it’s basically how SEO ruined traditional search. I think it’s also a big reason that a LOT of software engineers put way too much emphasis on passing unit tests and not nearly enough on examining what they’re actually testing.
Good points. What’s novel information vs. wrong information? (And subtly wrong is harder to understand than very wrong)
At some point it’s hitting a user who is giving feedback, but I imagine data lineage once it gets to the end user its tricky to understand.
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Hey now, if you hand everyone a “Hitler” card in Secret Hitler, it plays very strangely but in the end everyone wins.
…except the Jews.
The problem is the harvesting.
In previous incarnations of this process they used curated data because of hardware limitations.
Now that hardware has improved they found if they throw enough random data into it, these complex patterns emerge.
The complexity also has a lot of people believing it’s some form of emergent intelligence.
Research shows there is no emergent intelligence or they are incredibly brittle such as this one. Not to mention they end up spouting nonsense.
These things will remain toys until they get back to purposeful data inputs. But curation is expensive, harvesting is cheap.
Isn’t “intelligence” so ill defined we can’t prove it either way. All we have is models doing better on benchmarks and everyone shrieking “look emergent intelligence”.
I disagree a bit on “toys”. Machine summarization and translation is really quite powerful, but yeah that’s a ways short of the claims that are being made.
Remember before they were released and the first we heard of them, were reports on the guy training them or testing or whatever, having a psychotic break and freaking out saying it was sentient. It’s all been downhill from there, hey.
Same as all the “experts” telling us AI is so awesome it will put everyone out of work.
I thought it was so comically stupid back then. But a friend of mine said this was just a bullshit way of hyping up AI.
That tracks. And It’s kinda on brand, still. Skeezy af.
Thats a price you pay for all the indiscriminate scraping
Is there some way I can contribute some poison?
Steve Martin them, talk wrong.
Has anyone really been far even as decided to use even go want to do look more like?
What for can do a be taking is to poppies but did I for when going was to be a thing?
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I’m going to take this from a different angle. These companies have over the years scraped everything they could get their hands on to build their models, and given the volume, most of that is unlikely to have been vetted well, if at all. So they’ve been poisoning the LLMs themselves in the rush to get the best thing out there before others do, and that’s why we get the shit we get in the middle of some amazing achievements. The very fact that they’ve been growing these models not with cultivation principles but with guardrails says everything about the core source’s tainted condition.
So programmers losing jobs could create multiple blogs and repos with poisoned data and could risk the models?
So if someone was to hypothetically label an image in a blog or a article; as something other than what it is?
Or maybe label an image that appears twice as two similar but different things, such as a screwdriver and an awl.
Do they have a specific labeling schema that they use; or is it any text associated with the image?
Garbage in, garbage out.
I don’t know that it’s wise to trust what anthropic says about their own product. AI boosters tend to have an “all news is good news” approach to hype generation.
Anthropic have recently been pushing out a number of headline grabbing negative/caution/warning stories. Like claiming that AI models blackmail people when threatened with shutdown. I’m skeptical.
They’ve been doing it since the start. OAI was fear mongering about how dangerous gpt2 was initially as an excuse to avoid releasing the weights, while simultaneously working on much larger models with the intent to commercialize. The whole “our model is so good even we’re scared of it” shtick has always been marketing or an excuse to keep secrets.
Even now they continue to use this tactic while actively suppressing their own research showing real social, environmental and economic harms.
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Well, I’m still glad offline LLM’s exist. The models we download and store are way less popular then the mainstream, perpetually online ones.
Once I beef up my hardware (which will take a while seeing how crazy RAM prices are), I will basically forgo the need to ever use an online LLM ever again, because even now on my old hardware, I can handle 7 to 16B parameter models (quantized, of course).
I used to think it wasn’t viable to poison llms, but are you saying there’s a chance? [a meme comes to mind]
Isn’t this applicable to all human societies as well though?
