Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
Tianqing Fang, Zeming Chen, Yangqiu Song, Antoine Bosselut · 2024
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 COM 2 (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 COM 2 exhibit significant improvements in complex reasoning ability, resulting in enhanced zero-shot performance in both indomain and out-of-domain tasks for question answering and generative commonsense reasoning, without expensive human annotations. 1* Work done during internship at EPFL. 1 Code and data are available at https://github.com/ tqfang/complex-commonsense-reasoning