Abstract:

How can we better use our data to build more reliable and responsible models? Dr Koh will first discuss when it might be useful to train on synthetic image data derived, in turn, from a generative model trained on the available real data. Next, he will describe how scaling up the datastore for retrieval-based language models can significantly improve performance, indicating that the amount of data used at inference time—and not just at training time—should be considered as a new dimension of scaling language models. Finally, he will discuss how the static nature of most of our training data leads to language model failures in interactive settings, and present an adaptive conformal inference method to accurately quantify uncertainty in such dynamic environments.