Sequence Generation Model

Sequence generation models aim to create new sequences of data, such as text, code, or chemical structures, by learning patterns from existing data. Current research focuses on improving model efficiency and robustness, particularly through techniques like reinforcement learning, incorporating spatial information (e.g., in document parsing), and developing methods for better evaluation and interpretability. These advancements are driving progress in diverse applications, including anomaly detection, document understanding, machine translation, and even scientific reasoning tasks, ultimately leading to more powerful and reliable AI systems.