Hey now, if you hand everyone a “Hitler” card in Secret Hitler, it plays very strangely but in the end everyone wins.
Comment on It Only Takes A Handful Of Samples To Poison Any Size LLM, Anthropic Finds
Gullible@sh.itjust.works 21 hours agoI 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
UnderpantsWeevil@lemmy.world 20 hours ago
saltesc@lemmy.world 21 hours ago
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 with Google skill vs Google skill is a rich source of A+++ knowledge and social behaviour.
yes_this_time@lemmy.world 20 hours ago
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.
wizardbeard@lemmy.dbzer0.com 19 hours ago
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.
yes_this_time@lemmy.world 18 hours ago
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
hoppolito@mander.xyz 20 hours ago
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.
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.
WhiteOakBayou@lemmy.world 19 hours ago
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.
yes_this_time@lemmy.world 19 hours ago
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.