Pre Trained Generative Language Model

Pre-trained generative language models (PLMs) are powerful tools for various natural language processing tasks, aiming to generate human-quality text and improve comprehension. Current research focuses on enhancing their control and accuracy, particularly in addressing issues like "out-of-control generation" and sensitivity to prompt formatting, often employing techniques like question-attended span extraction (QASE) and fine-tuning on specialized datasets. These advancements are significant because they improve the reliability and efficiency of PLMs across diverse applications, from machine reading comprehension and biomedical text mining to cheminformatics and question answering systems, while also exploring more efficient model architectures like spiking neural networks.