This article provides an overview of the methods used for algorithmic text analysis in
economics, with a focus on three key contributions. First, we introduce methods for …

The explosive growth in fake news and its erosion to democracy, justice, and public trust has
increased the demand for fake news detection and intervention. This survey reviews and …

In this paper, we introduce a novel and simple method for obtaining high-quality text
embeddings using only synthetic data and less than 1k training steps. Unlike existing …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

We present modality gap, an intriguing geometric phenomenon of the representation space
of multi-modal models. Specifically, we show that different data modalities (eg images and …

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust
understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in …

This paper presents SimCSE, a simple contrastive learning framework that greatly advances
state-of-the-art sentence embeddings. We first describe an unsupervised approach, which …

Pre-trained contextual representations like BERT have achieved great success in natural
language processing. However, the sentence embeddings from the pre-trained language …

Recommending purely cold-start items is a long-standing and fundamental challenge in the
recommender systems. Without any historical interaction on cold-start items, the …

Pre-training models such as BERT have achieved great success in many natural language
processing tasks. However, how to obtain better sentence representation through these pre …