LinkBERT is a new language model pretrained to capture document link knowledge such as hyperlinks of the web. It greatly helps knowledge-intensive applications such as question answering.
Machine learning models that achieve high overall accuracy often make systematic errors on coherent slices of validation data. Can we develop methods to automatically identify these systematic errors?
Where do the rewards for robotic reinforcement learning come from? In this blog post we study how using crowdsourced language annotations and videos of humans, we can learn reward functions in a scalable way and enable them to generalize more broadly.
