video1.0<iframe src="https://www.loom.com/embed/3f2f15f3b1c74605b8f933b880200eb8" frameborder="0" width="1280" height="960" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9601280Loomhttps://www.loom.com9601280https://cdn.loom.com/sessions/thumbnails/3f2f15f3b1c74605b8f933b880200eb8-00001.gif165.483In this video, I will be discussing problem 960, which focuses on studying learned features in language models. The goal is to perform dimensionality reduction on neural activations across a large amount of text. This topic is important in terms of interpretability, as we want to understand how interpretable the resulting plots are. I will explain how we separate positive and negative comments in vector space and visualize them using a PCA of two dimensions. The quality of the model directly affects the effectiveness of the separation. Join me as we explore this fascinating problem!