The real world use cases for LLM poisoning is to attack places where those models are used via API on the backend, for data classification and fuzzy logic tasks (like a security incident prioritization in a SOC environment). There are no thumbs down buttons in the API and usually there's the opposite – promise of not using the customer data for training purposes.
The question was where should users draw the line? Producing gibberish text is extremely noticeable and therefore not really a useful poisoning attack instead the goal is something less noticeable.
Meanwhile essentially 100% of lengthy LLM responses contain errors, so reporting any error is essentially the same thing as doing nothing.
Reporting doesn't scale that well compared to training and can get flooded with bogus submissions as well. It's hardly the solution. This is a very hard fundamental problem to how LLMs work at the core.
Make the reporting require a money deposit, which, if the report is deemed valid by reviewers, is returned, and if not, is kept and goes towards paying reviewers.
You're asking people to risk losing their own money for the chance to... Improve someone else's LLM?
I think this could possibly work with other things of (minor) value to people, but probably not plain old money. With money, if you tried to fix the incentives by offering a potential monetary gain in the case where reviewers agree, I think there's a high risk of people setting up kickback arrangements with reviewers to scam the system.
... You want users to risk their money to make your product better? Might as well just remove the report button, so we're back at the model being poisoned.
Your solutions become more and more unfeasable. People would report less or anything at all if it costs money to do so, defeating the whole purpose of a report function.
And if you think you're being smart by gifting them money or (more likely) your "in-game" currency for "good" reports, it's even worse! They will game the system when there's money to be made, who stops a bad actor from reporting their own poison? Also who's going to review the reports and even if they finance people or AI systems to do that, isn't that bottlenecking new models if they don't want the poison training data to grow faster than it can be fixed? Let me make a claim here: nothing beats fact checking humans to this day or probably ever.
You got to understand that there comes a point when you can't beat entropy! Unless of course you live on someone else's money. ;)
Next pretrain iteration gets sanitized.