# Large Language Models estimate fine-grained human color-concept associations

arXiv: https://arxiv.org/abs/2406.17781 · HTML: https://mlsanity.com/paper/2406.17781/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 60 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Large Language Models as General Pattern Machines](https://mlsanity.com/paper/2307.04721/) (2023)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://mlsanity.com/paper/2109.06129/) (2021)
- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [Can language models handle recursively nested grammatical structures? A case study on comparing models and humans](https://mlsanity.com/paper/2210.15303/) (2022)
- [Semantic features of object concepts generated with GPT-3](https://mlsanity.com/paper/2202.03753/) (2022)
- [Colors -Messengers of Concepts: Visual Design Mining for Learning Color Semantics](https://mlsanity.com/paper/1505.06532/) (2015)
- [Semantic Discriminability for Visual Communication](https://mlsanity.com/paper/2009.03171/) (2020)
- [Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems](https://mlsanity.com/paper/2108.03685/) (2021)
- [Semantic Feature Verification in FLAN-T5](https://mlsanity.com/paper/2304.05591/) (2023)
- [Does Conceptual Representation Require Embodiment? Insights From Large Language Models](https://mlsanity.com/paper/2305.19103/) (2023)
- [Estimating Color-Concept Associations from Image Statistics](https://mlsanity.com/paper/1908.00220/) (2019)
- [Unifying Effects of Direct and Relational Associations for Visual Communication](https://mlsanity.com/paper/2209.02782/) (2022)

## Abstract (arXiv)

Concepts, both abstract and concrete, elicit a distribution of association strengths across perceptual color space, which influence aspects of visual cognition ranging from object recognition to interpretation of information visualizations. While prior work has hypothesized that color-concept associations may be learned from the cross-modal statistical structure of experience, it has been unclear whether natural environments possess such structure or, if so, whether learning systems are capable of discovering and exploiting it without strong prior constraints. We addressed these questions by investigating the ability of GPT-4, a multimodal large language model, to estimate human-like color-concept associations without any additional training. Starting with human color-concept association ratings for 71 color set spanning perceptual color space (textttUW-71) and concepts that varied in abstractness, we assessed how well association ratings generated by GPT-4 could predict human ratings. GPT-4 ratings were correlated with human ratings, with performance comparable to state-of-the-art methods for automatically estimating color-concept associations from images. Variability in GPT-4's performance across concepts could be explained by specificity of the concept's color-concept association distribution. This study suggests that high-order covariances between language and perception, as expressed in the natural environment of the internet, contain sufficient information to support learning of human-like color-concept associations, and provides an existence proof that a learning system can encode such associations without initial constraints. The work further shows that GPT-4 can be used to efficiently estimate distributions of color associations for a broad range of concepts, potentially serving as a critical tool for designing effective and intuitive information visualizations.