WizardEvent: Empowering Event Reasoning by Hybrid Event-Aware Data Synthesizing

Zhengwei Tao, Xiancai Chen, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Wenpeng Hu, Chongyang Tao, Shuai Ma

Abstract: Event reasoning is to reason with events and certain inter-event relations. These cutting-edge techniques possess crucial and fundamental capabilities that underlie various applications. Large language models (LLMs) have made advances in event reasoning owing to their wealth of training. However, the LLMs commonly used today still do not consistently demonstrate proficiency in managing event reasoning as humans. This discrepancy arises from not explicitly modeling events and their relations and insufficient knowledge of event relations. In addition, the different reasoning paradigms of the LLMs are trained in an imbalanced way. In this paper, we propose $\textsc {WizardEvent}$, to synthesize data from the unlabeled corpus with the proposed hybrid event-aware instruction tuning. Specifically, we first represent the events and their relation in a novel structure and then extract the knowledge from the raw text. Second, we introduce hybrid event reasoning paradigms with four reasoning formats. Lastly, we wrap our constructed event relational knowledge with the paradigms to create the instruction tuning dataset. We fine-tune the model with this enriched dataset, significantly improving the event reasoning. The performance of $\textsc {WizardEvent}$ is rigorously evaluated through extensive experiments. The results demonstrate that $\textsc {WizardEvent}$ substantially outperforms baselines, indicating the effectiveness of our approach.

External IDs:dblp:journals/tkde/TaoCJBZHTM26