# Non-literal Understanding of Number Words by Language Models

arXiv: https://arxiv.org/abs/2502.06204 · HTML: https://mlsanity.com/paper/2502.06204/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://mlsanity.com/paper/2004.03066/) (2020)
- [Testing the Ability of Language Models to Interpret Figurative Language](https://mlsanity.com/paper/2204.12632/) (2022)
- [PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities](https://mlsanity.com/paper/2401.07078/) (2024)
- [The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs](https://mlsanity.com/paper/2210.14986/) (2022)
- [Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models](https://mlsanity.com/paper/2209.08141/) (2022)
- [Are LLMs good pragmatic speakers?](https://mlsanity.com/paper/2411.01562/) (2024)

## Built on by (most cited first, 9 of 9)

- [On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts](https://mlsanity.com/paper/2509.06952/) (2025)
- [Instrument Effects in Language-Model Honesty Evaluation: An Auditable Single-System Demonstration](https://mlsanity.com/paper/2607.14399/) (2026)
- [(RSA)2: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understanding](https://mlsanity.com/paper/2506.09301/) (2025)
- [Pragmatics beyond humans: meaning, communication, and LLMs](https://mlsanity.com/paper/2508.06167/) (2025)
- [Social Meaning in Large Language Models: Structure, Magnitude, and Pragmatic Prompting](https://mlsanity.com/paper/2604.02512/) (2026)
- [Continuous Interpretive Steering for Scalar Diversity](https://mlsanity.com/paper/2604.07006/) (2026)
- [PragReST: Self-Reinforcing Counterfactual Reasoning for Pragmatic Language Understanding](https://mlsanity.com/paper/2606.18624/) (2026)
- [Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives](https://mlsanity.com/paper/2607.18443/) (2026)
- [CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition](https://mlsanity.com/paper/2609.21259/) (2026)

## Abstract (arXiv)

Humans naturally interpret numbers non-literally, effortlessly combining context, world knowledge, and speaker intent. We investigate whether large language models (LLMs) interpret numbers similarly, focusing on hyperbole and pragmatic halo effects. Through systematic comparison with human data and computational models of pragmatic reasoning, we find that LLMs diverge from human interpretation in striking ways. By decomposing pragmatic reasoning into testable components, grounded in the Rational Speech Act framework, we pinpoint where LLM processing diverges from human cognition -- not in prior knowledge, but in reasoning with it. This insight leads us to develop a targeted solution -- chain-of-thought prompting inspired by an RSA model makes LLMs' interpretations more human-like. Our work demonstrates how computational cognitive models can both diagnose AI-human differences and guide development of more human-like language understanding capabilities.