Video Summary

The paper proposes a new method for training language models, which is based on the idea of self-supervised learning. The method is called "self-supervised learning with contrastive estimation", or "SimCSE" for short. SimCSE works by comparing a given input sequence to a set of negative examples, and then training the model to predict which example is the positive one. This approach has been shown to be effective for training language models on a variety of tasks, including text classification, natural language inference, and question answering.