Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives
Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs' performance is sensitive to prompt format and the degree of surface similarity between narratives. This gap motivates a key question: What is the impact of enhancing structural mapping with LLM-derived abstractions on their analogical reasoning ability in narratives? To that end, we propose a modular framework named YARN (Yielding Abstractions for Reasoning in Narratives), which uses LLMs to dec...
| Authors | Mohammadhossein Khojasteh, Yifan Jiang, Stefano De Giorgis et al. |
|---|---|
| Primary category | cs.CL |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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