Ilya Shumailov

Exactly. And then we also have empirical errors from, for example, hardware. So we also have practical limitations of hardware with which we work. And those errors also exist.

Exactly. And then we also have empirical errors from, for example, hardware. So we also have practical limitations of hardware with which we work. And those errors also exist.

Yes, certainly. So what we observe in simple theoretical models is that two main phenomena happen. The very first phenomena that happens is it's really hard to approximate improbable events, in part because you don't encounter them very often. So you may discover that you're collecting more and more data, and a lot of this data looks very similar to what you already possess.

Yes, certainly. So what we observe in simple theoretical models is that two main phenomena happen. The very first phenomena that happens is it's really hard to approximate improbable events, in part because you don't encounter them very often. So you may discover that you're collecting more and more data, and a lot of this data looks very similar to what you already possess.

Yes, certainly. So what we observe in simple theoretical models is that two main phenomena happen. The very first phenomena that happens is it's really hard to approximate improbable events, in part because you don't encounter them very often. So you may discover that you're collecting more and more data, and a lot of this data looks very similar to what you already possess.

So you're not discovering too much information. But importantly, you're not discovering those infrequent data points. So those tail events, they kind of disappear. And then the other thing that happens is that the first time you made this error and underestimated your improbable events... When you hit the model on top of this, it's unlikely to recover from this taking place.

So you're not discovering too much information. But importantly, you're not discovering those infrequent data points. So those tail events, they kind of disappear. And then the other thing that happens is that the first time you made this error and underestimated your improbable events... When you hit the model on top of this, it's unlikely to recover from this taking place.

So you're not discovering too much information. But importantly, you're not discovering those infrequent data points. So those tail events, they kind of disappear. And then the other thing that happens is that the first time you made this error and underestimated your improbable events... When you hit the model on top of this, it's unlikely to recover from this taking place.

Originally improbable events are even more improbable for the subsequent model, and it kind of like snowballs out of control until the whole thing just collapses fully to near zero variance.

Originally improbable events are even more improbable for the subsequent model, and it kind of like snowballs out of control until the whole thing just collapses fully to near zero variance.

Originally improbable events are even more improbable for the subsequent model, and it kind of like snowballs out of control until the whole thing just collapses fully to near zero variance.

Exactly. And the thing is, you can theoretically describe this. It's actually very simple. And you can run these experiments however many times you want. And you'll discover that even if you have a lot of data, if you keep on repeating this process, and the rate at which this collapses, you can also bound, you end up always in a state where your improbable events kind of disappear.

Exactly. And the thing is, you can theoretically describe this. It's actually very simple. And you can run these experiments however many times you want. And you'll discover that even if you have a lot of data, if you keep on repeating this process, and the rate at which this collapses, you can also bound, you end up always in a state where your improbable events kind of disappear.

Exactly. And the thing is, you can theoretically describe this. It's actually very simple. And you can run these experiments however many times you want. And you'll discover that even if you have a lot of data, if you keep on repeating this process, and the rate at which this collapses, you can also bound, you end up always in a state where your improbable events kind of disappear.

In practice, when we grab large language models, we observe that they become more confident in the predictions that they are making. So basically, the improbable events here are going to be things that the model is not very confident about, and normally it would not make predictions about it.

In practice, when we grab large language models, we observe that they become more confident in the predictions that they are making. So basically, the improbable events here are going to be things that the model is not very confident about, and normally it would not make predictions about it.

In practice, when we grab large language models, we observe that they become more confident in the predictions that they are making. So basically, the improbable events here are going to be things that the model is not very confident about, and normally it would not make predictions about it.

So when you're trying to generate more data out of a language model in order for another language model to learn from it, over time, basically, it becomes more and more confident. And then it basically, during the generation setup, it gets stuck very often in these repetitive loops.

So when you're trying to generate more data out of a language model in order for another language model to learn from it, over time, basically, it becomes more and more confident. And then it basically, during the generation setup, it gets stuck very often in these repetitive loops.

So when you're trying to generate more data out of a language model in order for another language model to learn from it, over time, basically, it becomes more and more confident. And then it basically, during the generation setup, it gets stuck very often in these repetitive loops.