

MVP follows a standard Transformer encoder-decoder architecture.
MVP is supervised pre-trained using labeled datasets.
MVP also has task-specific soft prompts to stimulate the model's capacity in performing a certain task.
MVP is specially designed for natural language generation and can be adapted to a wide range of generation tasks, including but not limited to summarization, data-to-text generation, open-ended dialogue system, story generation, question answering, question generation, task-oriented dialogue system, commonsense generation, paraphrase generation, text style transfer, and text simplification. Our model can also be adapted to natural language understanding tasks such as sequence classification and (extractive) question answering.