Eve Bodnia

And then you're going to make predictions how the model is going to behave.

So in this case, it's the same situation.

We're going to find the minimal points of this energy landscape and we're going to oversee this landscape.

So you always have this bird view eye and you're going to know exactly where the right answers are.

And as you train in your model, you have ability to self-align it as well because sometimes your energy landscape is going to be a little bit off.

And just depending on what modeling you're using, we're using the model which has correction terms, so it can bring you back to the original landscape.

And of course, it's called perturbation theory, and technical people understand what I mean.

But typically, if you have a leading term and then you perturb it a little bit, you still can bring it back to where it was originally.

And it's a subject of delta, like how strong your perturbations are.

And you could define the perturbation as a subject of your environment and so on.

And this is what LLMs are.

Unfortunately, you'll never be able to do so just because the model is different, the architecture is different.

So this is the self-alignment part.

The hallucination-free part comes for the tasks where you want the answers to be precise, mathematically precise.

Obviously, you cannot formally verify poetry or similar tasks.

But if you want to verify your data analysis or you're generating the code and you want to make sure it's correct, here you can attach it to an external verifier like Lean4 and some people use it in other languages.

We personally use Lean4.

And then you can formalize the output and have your answer kind of checked on the level of compiler.

Yeah, so this is the whole point of energy-based model.

You never have to guess the next word.