this post was submitted on 16 Jan 2024
88 points (100.0% liked)
Technology
37746 readers
561 users here now
A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.
Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.
Subcommunities on Beehaw:
This community's icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
You don't need that many parameters, 4gb checkpoints work just fine.
For more inclusive models, or for current ones? In order to add something, either the size has to grow, or something would need to get pushed out (content, or quality). 4GB models are already at the limit of usefulness, both DALLE3 and SDXL run at about 12B parameters, so to make them "more inclusive" they'd have to grow.
I'm saying SD 1.5 and SDXL capture the concepts just fine, it's just during fine-tuning people train away some of the diversity.
Wait, by "fine-tuning"... do you mean LoRAs? Because those are more like brain surgery with a sledgehammer, rather the opposite of "fine". I don't think it's possible for LoRAs to avoid having undesirable side effects... and I don't think people even want that.
Actual "fine" tuning, would be adding the LoRA's training data to the original set, then training the whole model from scratch... and that would require increasing the model's size to encode the increased amount of data for the same output quality.
I mean like this. This paper just dropped the other day.
Nice read, and an interesting approach... although it kind of tries to hide the elephant in the room:
They show that the approach optimizes for less "stereotypes" and less "offensive", which in most cultures leads from worse to better "cultural representation"... but notice how there is a split in the "Indian" culture cohort, with an equal amount finding "more stereotypical, more offensive" to be just as good at "cultural representation":
They basically made the model more politically correct and "idealized", but in the process removed part of a culture representation that wasn't wrong, because the "culture" itself is split to begin with.
"Indian" is a huge population of very diverse people.
That's my point. They claim to reduce misrepresentation, while at the same time they erase a bunch of correct representations.
Going back to what I was saying: fine tuning doesn't increase diversity, it only shifts the biases. Encoding actual diversity would require increasing the model, then making sure it can output every correct representation.
It doesn't necessarily have to shift away from diversity biases. I think with care, you can preserve the biases that matter most. That was just their first shot at it, this seems like something you'd get better at over time.
I guess their main shortcoming was the cultural training set. I'm still unconvinced that level of fine tuning is possible without increasing model size, but we'll see what happens if/when someone curates a much larger set with cultural labeling.
The labels might also need to be more granular, like "culture:subculture:period", or something... which is kind of a snakes nest by itself.