Multimodal models are less reliable than text models

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 34:47

   I feel like you all obviously have released some, you know, stuff around images, right? And, you know, obviously multimodal, I imagine, is a focus. I think most of our listeners probably think of Jeb in the context of, like, you know, these decision models or type 1 things and kind of in classic, like, LLM replacement. Maybe talk a little bit about, like, the multimodal world, like, how you see the shape of problems there, how, like, Jeb models fit into those.

2. 35:11

   So there's a couple of, like, branches to go off of here. I will say branch one is that being aware of the reality of AI is so unbelievably important. And by its nature, just like long context tends to degrade intelligence no matter the model, multi-modality tends to degrade intelligence too. And this is like a well-known fact by like anyone in ML circles. And actually every modality has different rates at which it is degraded.

3. 35:37

   And this is very measurable stuff, except when you put them into chatbots, because chatbots are optimized for human preference. It becomes a very, very fuzzy thing on whether or not it's better or not. It's very measurable that multimodal models will be less reliable than the text models. And my philosophy has always been automate the easy work before the hard work. Like, will JEV models be used to automate software engineering?

4. 35:58

   Maybe one day. But software engineering, maybe I'm arrogant, seems a lot harder than like really easy tasks that LMs fail at. And in order to like be calibrated to the world, we should study why they fail at those easy tasks and like really honestly, like go in depth into studying them, not to cheat them, which I think is like quite common, but like actually like dig in and figure out what is missing and why.

5. 36:20

   And sometimes it's not the model. Sometimes it's the prompt. Sometimes it's every other stuff, you know, like it's a collaboration between software and model. But it's through that that I think we like learn the truth. I have, like, stories about how we got to even be anti-benchmaxing more. But this is a thing that LLMs try to avoid because they will look bad on benchmarks, as far as I can tell. And so that is one aspect of multimodality.

6. 36:45

   We have to take into account text tasks will be automated before the image tasks. Thing, too, is we invested really heavily on text tasks. And very much, like, we've done, like, a tiny amount of work in comparison to the image tasks. Like I am like kind of jazzed that our model does as well as it does. I think that that's great. Like as far as I can tell, our lead in images is not as large as our lead in text, but we do have an overall lead in images is my vibe.

7. 37:14

   Could be totally wrong, but that's kind of what I've sensed. And I would actually describe the image model as completely insufficient. Like I would be truly embarrassed to say that this is prod ready because there's so much jaggedness left in it that I'm jazzed if people find stuff to actually deploy. But I will say that this does not have like my seal of approval yet. We need to do so many rounds because like it's not quite as bad as like other people just shipping a shape of something.

8. 37:45

   But it's like in the grand scheme of it,