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.

语言理解中的基础是一个活跃的研究领域。以颜色感知和颜色语言为研究基础，通过实证研究发现，颜色空间和语言模型定义的特征空间之间存在相当大的一致性。通过收集包含近 100 万个颜色及其描述的大规模数据集，进行两种对齐方式的实证分析：（i）跨空间对齐，即学习嵌入空间和颜色空间之间的映射，以及（ii）内部空间对齐，通过提示颜色描述之间的比较。我们的结果表明，尽管对于单词基础、高度实用的颜色描述，颜色空间对齐成立，但在存在主观性和抽象性等真实语言使用元素的示例中，这种对齐性显著降低，这表明在这种情况下可能需要基础。

无所依托的 LLMs 中的知觉结构：抽象性和主观性在颜色语言中的影响

Perceptual Structure in the Absence of Grounding for LLMs: The Impact of  Abstractedness and Subjectivity in Color Language

The production of color language is essential for grounded language
generation. Color descriptions have many challenging properties: they can be
vague, compositionally complex, and denotationally rich. We present an
effective approach to generating color descriptions using recurrent neural
networks and a Fourier-transformed color representation. Our model outperforms
previous work on a conditional language modeling task over a large corpus of
naturalistic color descriptions. In addition, probing the model's output
reveals that it can accurately produce not only basic color terms but also
descriptors with non-convex denotations ("greenish"), bare modifiers ("bright",
"dull"), and compositional phrases ("faded teal") not seen in training.

使用递归神经网络和傅里叶变换的颜色表示方法，成功应用于生成颜色描述模型，在自然颜色描述的大型语料库上进行条件语言模型任务，不仅可以准确地生成基本颜色术语，还可以生成训练过程中未见过的具有非凸含义的描述词，裸修饰语以及组合短语。