北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (4): 77-82.

• 体系化人工智能专题 • 上一篇    下一篇

基于元多任务提示学习的零样本谣言检测方法

石 宇1, 于 宁1, 孙亚伟1, 刘建毅2   

  1. 1.北京邮电大学 可信分布式计算与服务教育部重点实验室;
    2.
    北京邮电大学 网络空间安全学院
  • 收稿日期:2023-12-21 修回日期:2024-02-23 出版日期:2024-08-28 发布日期:2024-08-26
  • 通讯作者: 刘建毅 E-mail:liujy@bupt.edu.cn
  • 基金资助:
    国家自然科学基金;北京邮电大学中央高校基本科研业务费项目

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

SHI Yu1, YU Ning1, SUN Yawei1, LIU Jianyi2   

  • Received:2023-12-21 Revised:2024-02-23 Online:2024-08-28 Published:2024-08-26

摘要: 针对现有谣言检测方法在微调大语言模型时导致的高内存占用,以及提示学习方法对初始点选择敏感的问题,提出一种基于元多任务提示学习的零样本谣言检测方法。首先,基于提示学习范式调整零样本谣言检测任务目标,通过设计提示模板使这一任务目标与大语言模型的训练任务目标保持一致,以充分利用大语言模型积累的先验知识。其次,利用元学习的参数更新策略以定位适用于零样本谣言检测任务的提示模板初始点,从不同的元任务中学习通用知识来实现参数优化。最后,引入情感分析作为辅助元任务进一步调整和优化模型参数。在多个公开数据集上的对比实验结果表明,所提方法在零样本谣言检测任务中表现出色,其性能指标优于基准方法。

关键词: 谣言检测, 提示学习, 元学习, 多任务学习

Abstract: 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.

Key words: rumor detection, prompt learning, meta learning, multi-task learning

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