Language Modelling

Language modeling aims to build computational models that can understand and generate human language, primarily by predicting the probability of sequences of words. Current research focuses on improving model efficiency and generalization, exploring architectures like Transformers and LSTMs, and investigating techniques such as masked and causal language modeling, data augmentation, and contrastive learning to enhance performance across diverse tasks and languages. These advancements have significant implications for various applications, including machine translation, speech recognition, and question answering, as well as for fundamental research into language understanding and representation.