Language Modeling Objective

Language modeling aims to train computational models to understand and generate human language, primarily through predicting the next word in a sequence (autoregressive models) or reconstructing masked words (masked language modeling). Current research focuses on improving efficiency (e.g., compressing long prompts, reducing model size), enhancing performance through multimodal learning (combining text and image data), and mitigating biases inherent in these models. These advancements are crucial for improving the accuracy and reliability of various natural language processing applications, including machine translation, question answering, and clinical text analysis, while also addressing ethical concerns around bias.