Technology

DO IT. Break that shit.

So, like with Godwin's law, the probability of a LLM being poisoned as it harvests enough data to become useful approaches 1.

I seriously keep reading LLM as MLM

So you're saying that thorn guy might be on to somthing?

I made this point recently in a much more verbose form, but I want to reflect it briefly here, if you combine the vulnerability this article is talking about with the fact that large AI companies are most certainly stealing all the data they can and ignoring our demands to not do so the result is clear we have the opportunity to decisively poison future LLMs created by companies that refuse to follow the law or common decency with regards to privacy and ownership over the things we create with our own hands.

Whether we are talking about social media, personal websites... whatever if what you are creating is connected to the internet AI companies will steal it, so take advantage of that and add a little poison in as a thank you for stealing your labor :)

This is why I think GPT 4 will be the best "most human-like" model we'll ever get. After that, we live in a post-GPT4 internet and all future models are polluted. Other models after that will be more optimized for things we know how to test for, but the general purpose "it just works" experience will get worse from here.

Is there some way I can contribute some poison?

Yea that's their entire purpose, to allow easy dishing of misinformation under the guise of

it's bleeding-edge tech, it makes mistakes

Thats a price you pay for all the indiscriminate scraping

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.

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.

And this is why I do the captchas wrong.

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?

There's a lot of research around this. So, LLM's go through phase transitions when they reach the thresholds described in Multispin Physics of AI Tipping Points and Hallucinations. That's more about predicting the transitions between helpful and hallucination within regular prompting contexts. But we see similar phase transitions between roles and behaviors in fine-tuning presented in Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs.

This may be related to attractor states that we're starting to catalog in the LLM's latent/semantic space. It seems like the underlying topology contains semi-stable "roles" (attractors) that the LLM generations fall into (or are pushed into in the case of the previous papers).

Unveiling Attractor Cycles in Large Language Models

Mapping Claude's Spirtual Bliss Attractor

The math is all beyond me, but as I understand it, some of these attractors are stable across models and languages. We do, at least, know that there are some shared dynamics that arise from the nature of compressing and communicating information.

Emergence of Zipf's law in the evolution of communication

But the specific topology of each model is likely some combination of the emergent properties of information/entropy laws, the transformer architecture itself, language similarities, and the similarities in training data sets.