# Representational Similarity Alignment of Figurative Language in Humans and Large Language Models

arXiv: https://arxiv.org/abs/2601.09041 · HTML: https://mlsanity.com/paper/2601.09041/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [A fine-grained comparison of pragmatic language understanding in humans and language models](https://mlsanity.com/paper/2212.06801/) (2022)
- [Language models align with human judgments on key grammatical constructions](https://mlsanity.com/paper/2402.01676/) (2024)
- [Testing AI on language comprehension tasks reveals insensitivity to underlying meaning](https://mlsanity.com/paper/2302.12313/) (2023)
- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)
- [An In-depth Look at Gemini's Language Abilities](https://mlsanity.com/paper/2312.11444/) (2023)
- [Does GPT-4 surpass human performance in linguistic pragmatics?](https://mlsanity.com/paper/2312.09545/) (2023)
- [Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing](https://mlsanity.com/paper/2510.04120/) (2025)

## Built on by (most cited first, 1 of 1)

- [Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning](https://mlsanity.com/paper/2608.12372/) (2026)

## Abstract (arXiv)

Large language models (LLMs) often produce judgments similar to those of humans, but their interpretation of figurative language remains unclear. We compared human participants and four instruction-tuned LLMs (GPT-4, Gemma-2-9B, Llama-3.2, and Mistral-7B) on their ratings of 240 dialogue-based sentences spanning six linguistic traits: conventionality, sarcasm, funny, emotion, idiomacy, and Gen Z slang. While humans and LLMs were strongly aligned at the surface level, differences emerged in representational similarity patterns, particularly in idiomacy and Gen Z slang. GPT-4 showed the closest alignment to humans, though all models differed at the representational level, suggesting that similar outputs may arise from different underlying language representations. We also examined the relationship between model scale and representational similarity using the Gemma model series (Gemma-2-9B-IT, Gemma-3-27B-IT, and Gemma-4-31B-IT), and found that representational similarity patterns were largely unaffected by model size.