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

arXiv: https://arxiv.org/abs/2209.08141 · HTML: https://mlsanity.com/paper/2209.08141/

## Key facts

- Cited by 14 papers in the map (data through 14 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.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [STaR: Bootstrapping Reasoning With Reasoning](https://mlsanity.com/paper/2203.14465/) (2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [Few-shot training LLMs for project-specific code-summarization](https://mlsanity.com/paper/2207.04237/) (2022)
- [Language Model Cascades](https://mlsanity.com/paper/2207.10342/) (2022)

## Built on by (most cited first, 14 of 14)

- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse](https://mlsanity.com/paper/2410.21333/) (2024)
- [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024)
- [AI-assisted coding: Experiments with GPT-4](https://mlsanity.com/paper/2304.13187/) (2023)
- [Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models](https://mlsanity.com/paper/2311.04915/) (2023)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Large Language Model Displays Emergent Ability to Interpret Novel Literary Metaphors](https://mlsanity.com/paper/2308.01497/) (2023)
- [Non-literal Understanding of Number Words by Language Models](https://mlsanity.com/paper/2502.06204/) (2025)
- [Are Large Language Models Sensitive to the Motives Behind Communication?](https://mlsanity.com/paper/2510.19687/) (2025)
- [Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification](https://mlsanity.com/paper/2501.02434/) (2025)
- [Conceptual Metaphor Theory as a Prompting Paradigm for Large Language Models](https://mlsanity.com/paper/2502.01901/) (2025)
- [THiNK: Can Large Language Models Think-aloud?](https://mlsanity.com/paper/2505.20184/) (2025)
- [MetaphorVU: Towards Metaphorical Video Understanding](https://mlsanity.com/paper/2605.25461/) (2026)

## 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.