Text Generation Model

Text generation models aim to create human-quality text automatically, encompassing tasks like summarization, translation, and open-ended generation. Current research emphasizes improving model accuracy, addressing issues like hallucinations (factual inaccuracies), bias, and the detection of malicious backdoors, often leveraging transformer-based architectures and techniques like contrastive learning and prompt engineering. These advancements have significant implications for various fields, including healthcare (e.g., automated report generation), journalism (e.g., scientific news summarization), and online education (e.g., personalized exercise creation), while also raising crucial ethical considerations regarding bias and misuse.