mlsanityData to 7 Oct 2026

Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models

arXiv 2209.08141 ↗First posted 16 Sep 2022cs.CL cs.AI cs.LGcited by 14

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Probabilistic models of language understanding are valuable tools for investigating human language use. However, they need to be hand-designed for a particular domain. In contrast, large language models (LLMs) are trained on text that spans a wide array of domains, but they lack the structure and interpretability of probabilistic models. In this paper, we use chain-of-thought prompts to introduce structures from probabilistic models into LLMs. We explore this approach in the case of metaphor understanding. Our chain-of-thought prompts lead language models to infer latent variables and reason about their relationships in order to choose appropriate paraphrases for metaphors. The latent variables and relationships chosen are informed by theories of metaphor understanding from cognitive psychology. We apply these prompts to the two largest versions of GPT-3 and show that they can improve performance in a paraphrase selection task.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 11 of its 27 references that are in the map. First posted on arXiv 16 Sep 2022.

Builds on

11 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  3. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  4. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 871
  5. 2022Using cognitive psychology to understand GPT-3cited by 389
  6. 2021True Few-Shot Learning with Language Modelscited by 343
  7. 2022Can language models learn from explanations in context?cited by 248
  8. 2022Language models show human-like content effects on reasoning taskscited by 171
  9. 2022Few-shot training LLMs for project-specific code-summarizationcited by 128
  10. 2022Language Model Cascadescited by 104

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 5; 2026: 1.

14 papers.

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  1. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  2. 2022On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoningcited by 191
  3. 2024Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worsecited by 107
  4. 2024Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challengescited by 48
  5. 2023AI-assisted coding: Experiments with GPT-4cited by 43
  6. 2023Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Modelscited by 24
  7. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  8. 2023Large Language Model Displays Emergent Ability to Interpret Novel Literary Metaphorscited by 12
  9. 2025Non-literal Understanding of Number Words by Language Modelscited by 9
  10. 2025Are Large Language Models Sensitive to the Motives Behind Communication?cited by 7
  11. 2025Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identificationcited by 3
  12. 2025Conceptual Metaphor Theory as a Prompting Paradigm for Large Language Modelscited by 3
2 more
  1. 2025THiNK: Can Large Language Models Think-aloud?cited by 3
  2. 2026MetaphorVU: Towards Metaphorical Video Understandingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory
  2. 2025Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
  3. 2024A Dual-Perspective Metaphor Detection Framework Using Large Language Models
  4. 2024Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation
  5. 2025Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing
  6. 2022MiQA: A Benchmark for Inference on Metaphorical Questions
  7. 2024Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks
  8. 2026Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models