Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. How well LLMs accomplish event reasoning on various relations and reasoning paradigms remains unknown. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs. We introduce a novel benchmark EV2 for EValuation of EVent reasoning. EV2 consists of two levels of evaluation of schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV2. We find that LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. We also notice the imbalance of event reasoning abilities in LLMs. Besides, LLMs have event schema knowledge, however, they're not aligned with humans on how to utilize the knowledge. Based on these findings, we introduce two methods to guide the LLMs to utilize the event schema knowledge. Both methods achieve improvements.

翻译：事件推理是支撑众多应用的基础能力。该能力需要事件模式知识以实现全局推理，并需应对事件间关系及推理范式的多样性。目前，大语言模型（LLMs）在不同关系类型与推理范式下的事件推理表现尚不明确。为弥合这一认知差距，我们系统评估了LLMs的事件推理能力，并提出新型基准测试EV2（事件推理综合评估）。EV2包含模式层级与实例层级双维度评估体系，覆盖全面的关系类型与推理范式。基于EV2的广泛实验表明：LLMs虽具备事件推理能力，但表现远未达到理想水平；同时发现LLMs存在事件推理能力失衡现象。尽管LLMs拥有事件模式知识，但人类与模型在知识运用方式上存在对齐偏差。基于上述发现，我们提出两种引导LLMs运用事件模式知识的方法，两种方法均取得显著效果提升。