Can Rumor Detection Enhance Fact Verification? Unraveling Cross-Task Synergies Between Rumor Detection and Fact Verification

Recently, rumor detection (fake news detection) has seen a surge in research interest, and fact verification (fake news checking) has simultaneously become a significant research aspect. Despite the inherent distinction between fact verification and rumor detection — the former being a three-category task and the latter a binary one — there has yet to be in-depth exploration into the synergies between these two tasks. Furthermore, given the severe scarcity and the time-consuming and costly construction nature of fact verification datasets, few-shot/zeroshot fact verification methods are particularly favored. To tackle these challenges, we conduct a series of studies around “How can rumor detection enhance few-shot fact verification, and to what extent?”. Specifically, we systematically investigate the knowledge transferability between the two tasks, proposing a framework, Det2Ver, that is applicable to both rumor detection and fact verification. Through the construction of adaptive prompt templates and prompt-tuned LLMs like T5, Det2Ver structurallevel synchronizes the two tasks and utilizes the external knowledge from rumor detection to reinforce fact verification task. We demonstrate the significance and effectiveness of Det2Ver. Through the few-shot/zero-shot experiments on three widelyused datasets, compared to other LLMs prompt-tuning baselines, the Det2Ver for cross-task knowledge augmentation brings a significant improvement in macro-F1 for fact verification