Tom Griffiths

So your large language model has to figure out everything that it knows about the world just from the sequences of words that it's seeing.

And also the kinds of things that a child is getting, you know, not just as a consequence of whatever those evolved constraints are and not just as a consequence of their broader experience, but

Also, the things that they're getting from being able to engage in using that language to produce desirable outcomes in the world around them, using it as a tool, not just something that you're necessarily learning to predict.

We do a little bit of that in our training of large language models at the end.

There's some fine-tuning about reinforcement learning and so on.

But I think that's a really interesting project for cognitive scientists is thinking about how to characterize

that gap that we have, right?

And using these sorts of models as a tool for working that out.

And so in my lab, we've done a bit of work.

This is most recently with Tom McCoy, who's now at Yale, looking at an approach that's called meta-learning for training neural networks.

And what meta-learning does is

It tries to create neural networks where we manipulate the initial weights that the neural network has in such a way that it's able to learn from less data.

Yeah, that's right.

And also coming closer to capturing those inductive biases and prior distributions.

So the way that it works, you say, I've got a bunch of different learning problems I want to solve.

In the linguistic case, you could think about this as, I'm going to want to learn lots of different languages.

I'm going to want to learn English.

I'm going to want to learn Korean.

I want to learn Urdu.

I want to learn each of the languages that you want to be able to learn.