Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-Trained neural network model to artificially synthesize new labeled data for supervised learning. We mainly focus on cases with scarce labeled data. Our method, referred to as language-model-based data augmentation (LAMBADA), involves fine-Tuning a state-of-The-Art language generator to a specific task through an initial training phase on the existing (usually small) labeled data. Using the fine-Tuned model and given a class label, new sentences for the class are generated. Our process then filters these new sentences by using a classifier trained on the original data. In a series of experiments, we show that LAMBADA improves classifiers performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the state-of-The-Art techniques for data augmentation, specifically those applicable to text classification tasks with little data.