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 (\texttt{UW-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.

通过研究人像的颜色概念关联评分与 GPT-4 评分的相关性，探讨了 GPT-4 能够准确预测人类颜色概念关联的能力，并证明了 GPT-4 可以有效估算各种概念的颜色关联分布，为设计有效且直观的信息可视化提供了重要工具。

大型语言模型估计细粒度人类色彩概念关联

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

Information visualizations such as bar charts and line charts are very common
for analyzing data and discovering critical insights. Often people analyze
charts to answer questions that they have in mind. Answering such questions can
be challenging as they often require a significant amount of perceptual and
cognitive effort. Chart Question Answering (CQA) systems typically take a chart
and a natural language question as input and automatically generate the answer
to facilitate visual data analysis. Over the last few years, there has been a
growing body of literature on the task of CQA. In this survey, we
systematically review the current state-of-the-art research focusing on the
problem of chart question answering. We provide a taxonomy by identifying
several important dimensions of the problem domain including possible inputs
and outputs of the task and discuss the advantages and limitations of proposed
solutions. We then summarize various evaluation techniques used in the surveyed
papers. Finally, we outline the open challenges and future research
opportunities related to chart question answering.

本篇综述论文旨在系统回顾当前关于数据可视化分析中图表问题回答（Chart Question Answering，CQA）系统的最新研究进展。论文采用分类法，从任务输入输出和解决方案等多个角度进行了整理和讨论，并总结了相关试验评估技术，同时还指出了与 CQA 相关的挑战和未来研究机遇。