Event commonsense reasoning requires the ability to reason about the
relationship between events, as well as infer implicit context underlying that
relationship. However, data scarcity makes it challenging for language models
to learn to generate commonsense inferences for contexts and questions
involving interactions between complex events. To address this demand, we
present COM2 (COMplex COMmonsense), a new dataset created by sampling multi-hop
logical queries (e.g., the joint effect or cause of both event A and B, or the
effect of the effect of event C) from an existing commonsense knowledge graph
(CSKG), and verbalizing them using handcrafted rules and large language models
into multiple-choice and text generation questions. Our experiments show that
language models trained on COM2 exhibit significant improvements in complex
reasoning ability, resulting in enhanced zero-shot performance in both
in-domain and out-of-domain tasks for question answering and generative
commonsense reasoning, without expensive human annotations.

通过从现有的常识知识图谱（CSKG）中采样多跳逻辑查询，并使用手工规则和大语言模型进行言语化，我们提出了 COM2（复杂常识）数据集，训练于此的语言模型在复杂推理能力方面取得了显著提升，从而在问题回答和生成常识推理的任务中表现出了增强的零样本性能，而无需昂贵的人工注释。