Analogical reasoning is a fundamental capacity of human cognition that allows us to reason abstractly about novel situations by relating them to past experiences. While it is thought to be essential for robust reasoning in AI systems, conventional approaches require significant training and/or hard-coding of domain knowledge to be applied to benchmark tasks. Inspired by cognitive science research that has found connections between human language and analogy-making, we explore the use of intuitive language-based abstractions to support analogy in AI systems. Specifically, we apply large pre-trained language models (PLMs) to visual Raven's Progressive Matrices (RPM), a common relational reasoning test. By simply encoding the perceptual features of the problem into language form, we find that PLMs exhibit a striking capacity for zero-shot relational reasoning, exceeding human performance and nearing supervised vision-based methods. We explore different encodings that vary the level of abstraction over task features, finding that higher-level abstractions further strengthen PLMs' analogical reasoning. Our detailed analysis reveals insights on the role of model complexity, in-context learning, and prior knowledge in solving RPM tasks.

翻译：类比推理是人类认知的基本能力，它使我们能够通过将新颖情境与过往经验相联系，对其进行抽象推理。尽管这一能力被认为对人工智能系统的稳健推理至关重要，传统方法仍需大量训练和/或领域知识的硬编码才能应用于基准任务。受认知科学研究中人类语言与类比创造之间关联的启发，我们探索了基于直觉性语言抽象来支持人工智能系统类比推理的可能性。具体而言，我们将大型预训练语言模型应用于视觉瑞文渐进矩阵测试——一种常见的关联推理测试。通过将问题的感知特征简单编码为语言形式，我们发现预训练语言模型展现出惊人的零样本关联推理能力，其表现超越人类水平，并接近基于监督视觉的方法。我们探索了不同编码方式（对任务特征的抽象程度各异），发现更高的抽象层次能进一步增强预训练语言模型的类比推理能力。详细分析揭示了模型复杂度、上下文学习及先验知识在解决瑞文渐进矩阵任务中的作用。