Thanks to the recent success of Pre-trained Language Models (PLMs), it has become a promising research direction to develop a universal model (UIE) that can solve all typical information extraction tasks within one generative framework.Nonetheless, in real-world scenarios of UIE applications, new data of different IE tasks and domains usually come in a stream over time.A desirable UIE system should be capable of continually learning new tasks without forgetting old ones, thereby allowing knowledge and functionalities expansion without retraining the whole system.In this paper, we study the UIE system under a more challenging yet practical scenario, i.e., "lifelong learning" settings, to evaluate its abilities in three aspects, including knowledge sharing and expansion, catastrophic forgetting prevention, and rapid generalization on few-shot and unseen tasks.To achieve these three goals, we present a novel parameter-and deployment-efficient prompt tuning method namely Lottery Prompt Tuning (LPT).LPT freezes the PLM's parameters and sequentially learns compact pruned prompt vectors for each task leveraging a binary prompt mask, while keeping the prompt parameters selected by the previous tasks insusceptible.Furthermore, we use a simple yet effective method to perform mask selection and show the powerful transferability of Lottery Prompts to novel tasks.Extensive experiments demonstrate that LPT consistently sets state-ofthe-art performance on multiple lifelong learning settings of UIE, including task-incremental setting on seen tasks, few-shot adaptation, and zero-shot generalization on novel tasks 1 .