# Iconicity in Large Language Models

arXiv: https://arxiv.org/abs/2501.05643 · HTML: https://mlsanity.com/paper/2501.05643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://mlsanity.com/paper/2004.03720/) (2020)
- [NEWTON: Are Large Language Models Capable of Physical Reasoning?](https://mlsanity.com/paper/2310.07018/) (2023)
- [Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5](https://mlsanity.com/paper/2305.04400/) (2023)
- [Inductive reasoning in humans and large language models](https://mlsanity.com/paper/2306.06548/) (2023)
- [Kiki or Bouba? Sound Symbolism in Vision-and-Language Models](https://mlsanity.com/paper/2310.16781/) (2023)
- [Quantifying syntax similarity with a polynomial representation of dependency trees](https://mlsanity.com/paper/2211.07005/) (2022)
- [With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models](https://mlsanity.com/paper/2409.14917/) (2024)

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

- [Cross-modal Associations in Vision and Language Models: Revisiting the Bouba-Kiki Effect](https://mlsanity.com/paper/2507.10013/) (2025)

## Abstract (arXiv)

Lexical iconicity, a direct relation between a word's meaning and its form, is an important aspect of every natural language, most commonly manifesting through sound-meaning associations. Since Large language models' (LLMs') access to both meaning and sound of text is only mediated (meaning through textual context, sound through written representation, further complicated by tokenization), we might expect that the encoding of iconicity in LLMs would be either insufficient or significantly different from human processing. This study addresses this hypothesis by having GPT-4 generate highly iconic pseudowords in artificial languages. To verify that these words actually carry iconicity, we had their meanings guessed by Czech and German participants (n=672) and subsequently by LLM-based participants (generated by GPT-4 and Claude 3.5 Sonnet). The results revealed that humans can guess the meanings of pseudowords in the generated iconic language more accurately than words in distant natural languages and that LLM-based participants are even more successful than humans in this task. This core finding is accompanied by several additional analyses concerning the universality of the generated language and the cues that both human and LLM-based participants utilize.