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    <abstract>We evaluate GPT-4o’s color naming across nine languages using both synthetic and human-derived stimuli. Using hue wheels, fixed basic categories, low-chroma hue lines, and dense binned CIELAB grids, we separate lexical availability of color terms from distributional agreement with human color naming. GPT-4o reliably names vivid, high-chroma colors and reproduces several known language-specific distinctions under constrained settings. However, its performance degrades sharply for low-chroma colors and for stimuli near human category boundaries. In these regions, model-human divergence remains high. Overall, GPT-4o shows strong cross-linguistic lexical knowledge but does not reliably match human color-naming distributions, especially in low-chroma and boundary regions.</abstract>
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%0 Conference Proceedings
%T Lexical Availability and Human Distributional Agreement in GPT-4o’s Color Naming
%A Feldman, Anna
%A Peng, Jing
%Y Mohammad, Saif M.
%Y Ousidhoum, Nedjma
%S Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-413-2
%F feldman-peng-2026-lexical
%X We evaluate GPT-4o’s color naming across nine languages using both synthetic and human-derived stimuli. Using hue wheels, fixed basic categories, low-chroma hue lines, and dense binned CIELAB grids, we separate lexical availability of color terms from distributional agreement with human color naming. GPT-4o reliably names vivid, high-chroma colors and reproduces several known language-specific distinctions under constrained settings. However, its performance degrades sharply for low-chroma colors and for stimuli near human category boundaries. In these regions, model-human divergence remains high. Overall, GPT-4o shows strong cross-linguistic lexical knowledge but does not reliably match human color-naming distributions, especially in low-chroma and boundary regions.
%U https://aclanthology.org/2026.starsem-conference.9/
%P 136-147

Markdown (Informal)

[Lexical Availability and Human Distributional Agreement in GPT-4o’s Color Naming](https://aclanthology.org/2026.starsem-conference.9/) (Feldman & Peng, *SEM 2026)

• Lexical Availability and Human Distributional Agreement in GPT-4o’s Color Naming (Feldman & Peng, *SEM 2026)

ACL

• Anna Feldman and Jing Peng. 2026. Lexical Availability and Human Distributional Agreement in GPT-4o’s Color Naming. In Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), pages 136–147, San Diego, California, United States. Association for Computational Linguistics.