Commonsense Reasoning Task

Commonsense reasoning tasks aim to equip artificial intelligence with the ability to understand and reason about everyday situations, a crucial step towards creating more human-like AI. Current research focuses on evaluating and improving the commonsense reasoning capabilities of large language models (LLMs) and multimodal LLMs using various benchmarks and prompting techniques, including chain-of-thought prompting, contrastive prompting, and methods that leverage knowledge graphs or tree-based preference learning. These efforts are significant because advancements in commonsense reasoning are essential for building more robust and reliable AI systems across numerous applications, from question answering to decision-making in complex scenarios. The field is actively exploring ways to move beyond superficial statistical correlations towards genuine understanding and reasoning.