Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLM s (2023)

Abstract: 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.

## introduction one of the most interesting aspects of large language models (llms) is that while they are usually trained without explicit linguistic supervision, or knowledge injection, there is a relevant body of work that shows that both linguistic structures and relational knowledge emerge even without the need for fine-tuning @xcite @xcite . this has generated an ongoing discussion on how these models are learning and if the current training objectives and text-only data are enough to cover a wide range of downstream tasks. one of the perspectives that have been used recently to study this phenomenon is color language @xcite . color naming is a relevant task @xcite , well understood in physiological @xcite and sociocultural terms @xcite and has an inherent intent from the communicative point of view @xcite . in a recent work, @xcite proposed to quantify the alignment, understood as the structural correspondence between a point in the color space (e.g. rgb or cielab) and its associated name in natural language represented as the feature vector obtained from a llm. for such empirical study, they make use of the color lexicon of american english @xcite , based on the munsell chart of color chips @xcite , finding that in general alignment is present across the color spectrum. while this work provides valuable insights on the actual grounding requirements, most of the color descriptions are monolexemic. the above findings sparked our interest in the issue and led us to run preliminary experiments to test to what degree this alignment exists for less pragmatic color descriptions. we observed that such alignment drops significantly in the presence of more complex and subjective ways to describe specific colors, for example, when the color names contain multiple nouns or nps, as well as terms with other parts-of-speech. ## related work color language, as a medium to study grounding, has been used in several works, mainly trying to learn a mapping between the descriptions and their associated points in color space, such as @xcite , based on character level model, @xcite ; @xcite which take as input a color representation and generates a natural language description, @xcite , who incorporates the idea of contextual information to guide the generation, and in @xcite tested it in a bilingual setting. @xcite ; @xcite proposed to model comparatives between colors. in most of these works, the source of color names come from @xcite , which compresses the results of an online survey where participants were asked to provide free-form labels in natural language to various rgb samples. this data was subsequently filtered by @xcite , leading to a total number of samples of over 2 million instances, but with a number of unique color names constrained to only 829. this suggests a reduction in the complexity of modeling tasks as proposed by previous work, as the vocabulary is fairly small, and with a homogeneous frequency. in contrast, the empirical study we propose does not have such constraint, allowing us to work with unique, subjective descriptions which are richer in vocabulary. in terms of using color to understand perceptual structure in llms, our direct inspiration was the work by @xcite , where authors perform experiments to quantify the alignment between points in color space and embeddings obtained by encoding the associated color names with llms. ## experimental setting data: we use data from colornames foot_1 , which crowdsources (color, color name) pairs. language models: for all experiments, we used three language models, namely, bert @xcite , roberta (liu et al., 2019), t5 @xcite , all of them in their large type, based on their availability on huggingface 4 . as a control baseline, we use fasttext word embeddings @xcite . the selection of such models is based on the need to explicitly extract embeddings from the llm, which means that in principle only white-box models can be used. this ruled out api-based systems like gpt-3 and other similar. moreover, even for competitive white-box models such as llama, given the size of our introduced dataset (around million examples), its usage is left for future work. finally, we note that our selection of llms lies within the masked-language modelling domain (mlm). this is a deliberate and critical decision, as it allows for our experiments to be performed in a controlled in-filling setting, limiting what the llm can output and allowing us to parse their generations automatically. more up-to-date models are all causal lms (with a few exceptions), which means that our capacity to control is more limited, as these models cannot in-fill text. moreover, it has been shown that the output of these models is highly dependent on how they are prompted, usually requiring a huge amount of work into prompt construction in order to control the output, which adds further complications. ## conclusion and future work we studied how llms encode perceptual structure in terms of the alignment between color and text embedding spaces and the inference of comparative relationships in a new, challenging dataset that encapsulates elements of real language usage, such abstractedness and subjectivity. the results show that lms perform in mixed way, which provides additional evidence on the need for actual grounding. in terms of future work, we are considering the need for additional contextual information, which could be attacked by considering color palettes instead of single colors, and also considering a multilingual approach, to cover the sociocultural aspects of color naming, specially in scenarios of low resource translation.