The content introduces COM2, a dataset created for complex commonsense reasoning using logical queries from CSKGs. It addresses the challenges of multi-hop reasoning and provides insights into training language models for enhanced performance across various tasks.
The paper discusses the construction of COM2, sampling multi-hop logical queries from CSKGs, and verbalizing them to create a benchmark for complex reasoning. Experiments show significant improvements in language models' reasoning abilities trained on COM2.
The study highlights the importance of addressing data scarcity in training language models for complex reasoning tasks. The results demonstrate the efficacy of leveraging existing knowledge graphs to enhance commonsense reasoning capabilities in AI systems.
Overall, the research contributes to advancing AI capabilities in complex commonsense reasoning through innovative dataset creation and model training strategies.
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by Tianqing Fan... um arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07398.pdfTiefere Fragen