ARN: A Comprehensive Framework and Dataset for Analogical Reasoning on Narratives

Analogical reasoning is one of the prime abilities of humans and is linked to creativity and scientific discoveries. This ability has been studied extensively in natural language processing (NLP) as well as in cognitive psychology by proposing various benchmarks and evaluation setups. Yet, a substantial gap exists between evaluations of analogical reasoning in cognitive psychology and NLP. Our aim is to bridge this by computationally adapting theories related to analogical reasoning from cognitive psychology in the context of narratives and developing an evaluation framework large in scale. More concretely, we propose the task of matching narratives based on system mappings and release the Analogical Reasoning on Narratives (ARN) dataset. To create the dataset, we devise a framework inspired by cognitive psychology theories about analogical reasoning to utilize narratives and their components to form mappings of different abstractness levels. These mappings are then leveraged to create pairs of analogies and disanalogies/distractors with more than 1k triples of query narratives, analogies, and distractors. We cover four categories of far/near analogies and far/near distractors that allow us to study analogical reasoning in models from distinct perspectives. In this study, we evaluate different large language models (LLMs) on this task. Our results demonstrate that LLMs struggle to recognize higher-order mappings when they are not accompanied by lower-order mappings (far analogies) and show better performance when all mappings are present simultaneously (near analogies). We observe that in all the settings, the analogical reasoning abilities of LLMs can be easily impaired by near distractors that form lower-order mappings with the query narratives.