Zero-Shot Rumor Detection via Meta Multi-Task Prompt Learning

To address the issue of the vast amount of memory usage associated with fine-tuning large language models in existing rumor detection methods, and to tackle the sensitivity of prompt learning to its initial point, a meta multi-task prompt learning method for zero-shot rumor detection is proposed. First, the objective of the zero-shot rumor detection task objective is modified based on the prompt learning, and the prompt template is designed to make its task objective align with the training task objective of large language models, fully leveraging the prior knowledge accumulated by large language models. Second, the parameter update strategy based on meta-learning is employed to rapidly identify suitable initial points of the prompt template for zero-shot rumor detection, and the meta-knowledge is learned from different meta-tasks to achieve parameter optimization. Finally, sentiment analysis is introduced as an auxiliary meta-task to further model parameter optimization. Extensive experiments conducted on real-world datasets demonstrate that the proposed model outperforms baseline methods in zero-shot rumor detection tasks, achieving the best performance across various metrics. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.