论文标题
探索人类智商测试中神经模型的空间推理能力
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests
论文作者
论文摘要
尽管神经模型在各种任务（例如图像识别和问答）上表现出色，但仅在很少的研究中衡量了其推理能力。在这项工作中，我们专注于空间推理并探索对神经模型的空间理解。首先，我们描述以下两个空间推理IQ测试：旋转和形状组成。使用定义明确的规则，我们构建了由各种复杂性级别组成的数据集。我们在概括方面设计了各种实验，并评估了新生成的数据集上的六个不同的基线模型。我们提供了影响模型概括能力的结果和因素的分析。此外，我们分析了神经模型如何使用视觉辅助工具解决空间推理测试。我们的发现将为理解机器和机器与人之间的差异提供宝贵的见解。
Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. Our findings would provide valuable insights into understanding a machine and the difference between a machine and human.
