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Found 5 papers, 1 papers with code
Probabilistic Analogical Mapping with Semantic Relation Networks
The human ability to flexibly reason using analogies with domain-general content depends on mechanisms for identifying relations between concepts, and for mapping concepts and their relations across analogs.
Visual analogy: Deep learning versus compositional models
We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations.
Zero-shot visual reasoning through probabilistic analogical mapping
Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs.
Emergent Analogical Reasoning in Large Language Models
In human cognition, this capacity is closely tied to an ability to reason by analogy.
Large Language Model Displays Emergent Ability to Interpret Novel Literary Metaphors
In addition, for several novel English poems GPT4 produced interpretations that were rated as excellent or good by a human literary critic.
