# Do Language Models Associate Sound with Meaning? A Multimodal Study of Sound Symbolism

arXiv: https://arxiv.org/abs/2511.10045 · HTML: https://mlsanity.com/paper/2511.10045/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 63 references that are in the map.
- First posted on arXiv 13 Nov 2025.

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

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small](https://mlsanity.com/paper/2211.00593/) (2022)
- [Same Task, Different Circuits: Disentangling Modality-Specific Mechanisms in VLMs](https://mlsanity.com/paper/2506.09047/) (2025)
- [Do large language models resemble humans in language use?](https://mlsanity.com/paper/2303.08014/) (2023)
- [A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models](https://mlsanity.com/paper/2502.17516/) (2025)
- [AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models](https://mlsanity.com/paper/2506.05140/) (2025)
- [HLB: Benchmarking LLMs' Humanlikeness in Language Use](https://mlsanity.com/paper/2409.15890/) (2024)
- [Measuring Sound Symbolism in Audio-visual Models](https://mlsanity.com/paper/2409.12306/) (2024)

## Built on by (most cited first, 3 of 3)

- [Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models](https://mlsanity.com/paper/2607.10162/) (2026)
- [ConlangBench: Exploring Language Knowledge and Learning in LLMs through Diverse Constructed Languages](https://mlsanity.com/paper/2608.03505/) (2026)
- [Similar Choices, Different Attention: Cross-Modal Associations in Humans and Vision-Language Models](https://mlsanity.com/paper/2609.36475/) (2026)

## Abstract (arXiv)

Sound symbolism is a linguistic concept that refers to non-arbitrary associations between phonetic forms and their meanings. We suggest that this can be a compelling probe into how Multimodal Large Language Models (MLLMs) interpret auditory information in human languages. We investigate MLLMs' performance on phonetic iconicity across textual (orthographic and IPA) and auditory forms of inputs with up to 25 semantic dimensions (e.g., sharp vs. round), observing models' layer-wise information processing by measuring phoneme-level attention fraction scores. To this end, we present LEX-ICON, an extensive mimetic word dataset consisting of 8,052 words from four natural languages (English, French, Japanese, and Korean) and 2,930 systematically constructed pseudo-words, annotated with semantic features applied across both text and audio modalities. Our key findings demonstrate (1) MLLMs' phonetic intuitions that align with existing linguistic research across multiple semantic dimensions and (2) phonosemantic attention patterns that highlight models' focus on iconic phonemes. These results bridge domains of artificial intelligence and cognitive linguistics, providing the first large-scale, quantitative analyses of phonetic iconicity in terms of MLLMs' interpretability.