mlsanityData to 14 Oct 2026

A Nurse is Blue and Elephant is Rugby: Cross Domain Alignment in Large Language Models Reveal Human-like Patterns

arXiv 2405.14863 ↗First posted 23 May 2024cs.CL cs.AI cs.LGcited by 2

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Abstract (arXiv)

Cross-domain alignment refers to the task of mapping a concept from one domain to another. For example, ``If a textitdoctor were a textitcolor, what color would it be?''. This seemingly peculiar task is designed to investigate how people represent concrete and abstract concepts through their mappings between categories and their reasoning processes over those mappings. In this paper, we adapt this task from cognitive science to evaluate the conceptualization and reasoning abilities of large language models (LLMs) through a behavioral study. We examine several LLMs by prompting them with a cross-domain mapping task and analyzing their responses at both the population and individual levels. Additionally, we assess the models' ability to reason about their predictions by analyzing and categorizing their explanations for these mappings. The results reveal several similarities between humans' and models' mappings and explanations, suggesting that models represent concepts similarly to humans. This similarity is evident not only in the model representation but also in their behavior. Furthermore, the models mostly provide valid explanations and deploy reasoning paths that are similar to those of humans.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

22 of 33 references.

References · 22

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10 more
  1. 2022Efficient Few-Shot Learning Without Promptscited by 137
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  3. 2021Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Modelscited by 78
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025LLM Agents Display Human Biases but Exhibit Distinct Learning Patternscited by 3
  2. 2026Teaching Values to Machines: Simulating Human-Like Behavior in LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024LLMs as Models for Analogical Reasoning
  2. 2025Modeling Understanding of Story-Based Analogies Using Large Language Models
  3. 2025Uncovering the Computational Ingredients of Human-Like Representations in LLMs
  4. 2025Cognitive Foundations for Reasoning and Their Manifestation in LLMs
  5. 2025Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking
  6. 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
  7. 2024A Flexible Method for Behaviorally Measuring Alignment Between Human and Artificial Intelligence Using Representational Similarity Analysis
  8. 2026Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning