Controllable Text Generation (CTG) is an emerging area in the field of natural language
generation (NLG). It is regarded as crucial for the development of advanced text generation …

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Data augmentation, the artificial creation of training data for machine learning by
transformations, is a widely studied research field across machine learning disciplines …

We propose Future Discriminators for Generation (FUDGE), a flexible and modular method
for controlled text generation. Given a pre-existing model G for generating text from a …

This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the
performance on text classification tasks. AEDA includes only random insertion of …

Large language models (LLMs) can be used to generate text data for training and evaluating
other models. However, creating high-quality datasets with LLMs can be challenging. In this …

Data Augmentation (DA) methods–a family of techniques designed for synthetic generation
of training data–have shown remarkable results in various Deep Learning and Machine …

Current large-scale language models can be politically biased as a result of the data they
are trained on, potentially causing serious problems when they are deployed in real-world …

GPT-3 is a large-scale natural language model developed by OpenAI that can perform many
different tasks, including topic classification. Although researchers claim that it requires only …