1. In-Context Analogical Reasoning with Pre-Trained Language Models
- Author
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Hu, Xiaoyang, Storks, Shane, Lewis, Richard L., and Chai, Joyce
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Computation and Language ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Computation and Language (cs.CL) ,Machine Learning (cs.LG) - Abstract
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.
- Published
- 2023
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