Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language
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Abstract (arXiv)
The need for grounding in language understanding is an active research topic. Previous work has suggested that color perception and color language appear as a suitable test bed to empirically study the problem, given its cognitive significance and showing that there is considerable alignment between a defined color space and the feature space defined by a language model. To further study this issue, we collect a large scale source of colors and their descriptions, containing almost a 1 million examples , and perform an empirical analysis to compare two kinds of alignments: (i) inter-space, by learning a mapping between embedding space and color space, and (ii) intra-space, by means of prompting comparatives between color descriptions. Our results show that while color space alignment holds for monolexemic, highly pragmatic color descriptions, this alignment drops considerably in the presence of examples that exhibit elements of real linguistic usage such as subjectivity and abstractedness, suggesting that grounding may be required in such cases.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 22 Nov 2023.
Builds on
14 of 28 references.
References · 14
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Citing papers per year: 2024: 2; 2025: 2.
4 papers.
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- 2025Color Bind: Exploring Color Perception in Text-to-Image Modelscited by 2
- 2025ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendationcited by 0
- 2025Color Bind: Exploring Color Perception in Text-to-Image Models27 Aug 2025
- 2025ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendation12 Aug 2025
- 2024Constructive Approach to Bidirectional Influence between Qualia Structure and Language Emergence14 Sep 2024
- 2024Reflectance Estimation for Proximity Sensing by Vision-Language Models: Utilizing Distributional Semantics for Low-Level Cognition in Robotics11 Apr 2024