Comment by lhl
21 hours ago
There have been other "universal"/general classifiers like GLiNER, GLiFormer, etc based on BERTs (Laya itself is based on ModernBERT!), but I do think there's something underrated about slapping classification on a "big" model like I've seen post-Jev announcement, lots of Qwen stuff, but the most interesting to me so far is razorback16/openjev using DiffusionGemma. There's a level of generalization that lots and lots of parameters get you that you can't really get out of small models.
> using DiffusionGemma.
That's an interesting choice. One question I had when looking at the jev copy on their blog is if one "line" in their output looks / attends to other lines. I think not, since they say it's parallel and not autoregressive. In that regard, it would be interesting to play with diffusion, and see if you'd get better results by playing with types, locking some, and so on.
> That's an interesting choice. One question I had when looking at the jev copy on their blog is if one "line" in their output looks / attends to other lines. I think not, since they say it's parallel and not autoregressive.
I don't understand the connection between the lack of autoregression and options attending to each other.
Non autoregressive models can attend to all the inputs simultanously.
An autogregressive model can can attend to all the options in the context of each other by simply writing the options out twice. Autoregressive models actually requires this, since one of them will come later, and the earlier prefill inputs can't attend to the later ones.