# Character Sequence Models for ColorfulWords

arXiv: https://arxiv.org/abs/1609.08777 · HTML: https://mlsanity.com/paper/1609.08777/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Multimodal Color Recommendation in Vector Graphic Documents](https://mlsanity.com/paper/2308.04118/) (2023)
- [Perceptual Structure in the Absence of Grounding for LLMs: The Impact of Abstractedness and Subjectivity in Color Language](https://mlsanity.com/paper/2311.13105/) (2023)
- [Generating Compositional Color Representations from Text](https://mlsanity.com/paper/2109.10477/) (2021)
- [Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?](https://mlsanity.com/paper/2506.11807/) (2025)
- [ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendation](https://mlsanity.com/paper/2508.08987/) (2025)

## Abstract (arXiv)

We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us.