Towards Bayesian World Models

Hello and welcome. This is Active Inference Guest Stream 65.1. It's December 6th, 2023. We'll be hearing from Phoebe Klett and Dan Simpson on Bayesian World Models for Explainable Transparent Reasoning. There will be a presentation followed by a discussion. So thank you for joining Phoebe. Very much. Looking forward to the presentation. To you. Awesome. Thanks for having us. Yeah, I'm really excited to chat with you all about how we might start to integrate today's state-of-the-art language models into more probabilistic machinery and what that might buy us. All right. Let's get right into it. All right. So some of the things I'm hoping to discuss today include why we might use a language model for something that isn't long-form text generation and how we might do that. And then motivate a little bit kind of why we might need a world model, what simple self-organizing world models might look like, and even in the simplest cases, how we might start to use those as effective recommendation engines in the wild today. And then a little bit of discussion about kind of where this research is going. All right. So what are language models good at? Today's especially large language models are trained on next token prediction. So this means given a sequence of tokens, we're going to estimate which token is most likely to come next. Maybe rephrasing that a little bit. We might also say that language models are trained to estimate which sequences of tokens or words are likely. The caveat being likely to appear in the training set. But today our training sets are quite extensive. So given this simple training objective, it's arguably surprising that we've seen language models do as well as they have in really impressive tasks. So they start to demonstrate like really great language understanding, meaning like the semantics of language itself, not just the syntax of kind of like which sentences make sense. They've also started to demonstrate some world knowledge. So implicitly learning kind of how the world works just through our own human abstraction and how we articulate that. We start to see language models falter at tasks which look more like symbolic problem solving. So math in particular, we see this in programming, although we're getting better at this. And in general, any kind of like long term planning tasks which require abstract reasoning. So we see this also in problems that look like this, which are word problems, but which are really about kind of like understanding abstractly how these kind of abstract objects relate to each other. And again, this shouldn't be surprising that language models struggle to do to do things like this since it's so far off from their original training objective. And in particular, even when we arrive at the right answer in some of these cases, it's really hard to know after the fact kind of the reasoning for how we ended up at the right answer in this particular case. And maybe more fundamentally, if we don't know where the abstraction is happening or where the reasoning is happening, it's very hard to guide that process. And so this starts to motivate the need for something that looks more like an explicit world model. And now to start to borrow some words from Yashua Bengio, this is one of the kind of biggest issues with today's language models is arguably that we're asking the language model to be both the inference machine and the world model implicitly when this doesn't quite make sense. And so some things that we might hope for in a world model are to model causal relationships, to be really adept at modeling using uncertainty and to be modular. Jan LeCun puts this in a similar way. So we might kind of ask a world model to be able to distinguish between which details are important versus irrelevant and to be able to make predictions that can be performed in sort of like this abstract space of representations. And so hopefully in the next following slides, we're going to motivate what it…