Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs
Authors: Pablo Loyola
Edison Marrese-Taylor
Andres Hoyos-Idrobo
Venue: EMNLP 2023 Findings (2023)
Keywords: Color language
grounding
language models
TL;DR: Exploring alignment between color and language spaces in the presence of abstractedness and subjectivity

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.

Perceptual Structure in the Absence of Grounding for LLMs: The Impact
of Abstractedness and Subjectivity in Color Language
Pablo Loyola', Edison Marrese-Taylor-2 Andres Hoyos-Idobro'
Rakuten Institute of Technology; Rakuten Group, Inc; Tokyo, Japan
2 National Institute of Advanced Industrial Science and Technology, Japan
Graduate School of Engineering, The University of Tokyo
Rakuten Institute of Technology; Rakuten Group, Inc; Paris, France @ @rakuten.com, emarrese weblab...u-tookyo......j
Abstract
terms (Gibson al., 2017) and has an inherent intent from the communicative point of view (Za- slavsky et al., 2019; Twomey et al., 2021). In a recent work, Abdou et al. (2021) proposed to quan- tify the alignment, understood as the structural cor- respondence between a point in the color space (e.g. RGB or CIELAB) and its associated name in natural language represented the feature vector obtained from LLM. For such empirical study, they make use of the Color Lexicon of American English (Lindsey and Brown, 2014), based on the Munsell Chart color chips (Munsell et al., 1915), finding that in general alignment is present across the color spectrum. While this work provides valu- able insights on the actual grounding requirements, most of the color descriptions are monolexemic.
The need for grounding in language understand- ing is an active research topic. Previous work has suggested that color perception and color language appear a suitable test bed to empiri- cally study the problem, given its cognitive sig- nificance and showing that there considerable alignment between defined color space and the feature space defined by language model. To further study this issue, we collect large scale source of colors and their descriptions. containing almost a million examples, and perform empirical analysis compare two kinds of alignments: (i) inter-space, by learn- ing mapping between embedding space and color space, and (ii) intra- intra-space, by means of prompting comparatives between color descrip- tions. Our results show that while color space alignment holds for monolexemic, highly prag- matic 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.
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 NPs, as well as terms with other parts- -peeech
1 Introduction
One of the most interesting aspects of Large Lan- guage Models (LLMs) is that while they are usu- ally trained without explicit linguistic supervision, 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 (Petroni et al., 2019; Goldberg, 2019; Marvin and Linzen, 2018). This has gener- ated 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.
Culor
Color Description
Calor
Color Description
Deep Tomato Blood
Froggy Happiness
Texsted Ranana Pulding
True Form of Sulness
Matcha With Milk
Bluseome laprncer April
Table 1: Selection of samples from the COLORNAMES dataset, showing the richness of color descriptions.
To further study these issues, construct more challenging test scenario by using and pro- cessing data from ColorNames', an online service where users can collaboratively generate (color, color description) pairs. Given its free-form struc- ture, the COLORNAMES dataset represents a rich
One of the perspectives that have been used re- cently study this phenomenon is color language (Abdou al., 2021). Color naming is a relevant task (Steels et al., 2005), well understood in phys- iological (Loreto et al., 2012) and sociocultural
Intpss//ccoloramess.ogg

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and heterogeneous corpus of color descriptions. This can be appreciated clearly in Table 1.
take as input color representation and generates natural language description, Monroe et al. (2017), who incorporates the idea of contextual informa- tion to guide the generation, and in Monroe et al. (2018) tested in a bilingual setting. Winn and Muresan (2018); Han et al. (2019) proposed to model comparatives between colors. In most of these works, the source of color names come from Munroe (2010), which compresses the results of online survey where participants were asked to pro- vide free-form labels in natural language to various RGB samples. This data was subsequently filtered by McMahan and Stone (2015), 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 reduction in the com- plexity of modeling tasks as proposed by previous work, as the vocabulary is fairly small, and with a homogeneous frequency. contrast, the empirical study we propose does not have such constraint, allowing us to work with unique, subjective de- scriptions which are richer in vocabulary. In terms of using color to understand perceptual structure in LLMs, our direct inspiration was the work by Abdou et al. (2021), where authors perform experi- ments quantify the alignment between points in color space and embeddings obtained by encoding the associated color names with LLMs.
Using this new, more challenging dataset, we conducted two experiments. The first one comple- ments the work of Abdou et al. (2021) in assess- ing the inter-space alignment between color and LLM spaces, with two key new additions: (i) propose a more ane-grained color name segmenta- tion, considering metrics associated to subjectivity and concreteness, and (ii) we adopt an Optimal Transport-based metric to complement the existing alignment methods. For the second experiment, we focus on the representations obtained from the LLMs and their ability to ground color compara- tives way structure color descriptions. Crit- ically, we do this without the need of accessing underlying points in color space. Concretely, we assess to what extent the LLM is able to discover a comparative- based relationship between a pair of color names without the need for explicit un- derlying color information, following a few shot learning configuration. For example, what would be the correct comparative associated to the pair of color names (e.g. between blood red wine and funeral roses)? If the model succeeds on that task, it could mean that such relationships are somehow encoded during pretraining, without the need of am explicit color signal.
The results of the proposed experiments show in general the alignment scores between spaces on the proposed dataset are low, contrasting with the re- sults provided on (Abdou et al., 2021) on the Mun- sell dataset. This means that the complexity of the color descriptions, exemplified by the subjectivity and concreteness metrics, really impact on the per- ceptual structure the language models achieve. On the other hand, the results of the second experiment on comparative prediction, show that all language models are able to perform surprisingly well, even in scenarios of high subjectivity. This discrepancy leads to think that somehow the models retain cer- tain structure learned thought language, but are not able to translate into color modality.
3 Experimental Setting
Data: We use data from ColorNames?, which crowdsources (color, color name) pairs. Table 1 presents some examples. The extracted data was fil- tered standardize the comparison. Only English sentences were kept, spam entries were removed using predefined rules and only color names with a maximum of five words were kept. The resulting dataset consists of 953,522 pair instances, with a total vocabulary size of 111,531 tokens. As seen in Table 2, words with the highest frequencies cor- respond to color words. In terms of POS patterns, the data presents a total of 3,809 combinations, tracted using Spacy² but the most frequent patterns represent ways to modify nouns, by using an adjec- tive (e.g. dark apple) or a sequence of nouns. We computed concreteness scores for the descriptions based on Brysbaert et al. (2014), which provides a defined set of lemmas with ranking varying from I to 5. For example, red pine brown gets a score
2 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 Kawakami et al. (2016), based on character level model, Mon- roe et al. (2016); McMahan and Stone (2015) which
2 "colornames.om 3 httpss///fpee..oollssgggggisssss

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ugly rich
apple
Word
Frequency
POS Pattern
Frequency
1.0
green
56,542
CADI' 'NOUN"
119,752
0.8
1000
pupple
40,272
('NOUNN. 'NOUN")
78,678
blue
29,880
('VERB', 'NOUN')
64,720
750
pink 20,878
(PPROPN,, 'NOUN')
55,142
500
red
C'AD'. 'NOUN. 'NOUN')
53,349
0.4
yellow
15.135
(PROPN'. 'PROPN')
42,701
Table 2: Most common words and most common POS patterns across color descriptions.
0.0
Concreteness scare
Figure Joint histograms showing subjectivity and concreteness scores for color descriptions containing the terms ugly, rich and apple, respectively.
up-to-date models are all causal LMs (with few exceptions), which means that capacity trol is more limited, these models cannot in-fill text. Moreover, has been shown that the output of these models is highly dependent on how they are prompted, usually requiring a huge amount work into prompt construction in order to control the output, which adds further complications.
of 4.3, while mysterious skyscape gets a score of 1.9. In this sense, we make the assumption that lower concreteness means higher abstractedness. Additionally, subjectivity scores were computed based on TextBlob (Loria et al., 2018), rule-based approach that provides score between and 1, where a description like thick and creamy gets a score of 0.47, and mathematically perfect purple gets a 1.0. Figure 3 shows the correspondence be- tween the scores and the expected usage of three sample words, ranging from ugly, term associ- ated with subjectivity to apple, which is commonly used to represent the reds spectrum. In the case of rich, it could have mixed connotations, which is reflected in its histogram having most frequent values the center.
Experiments
Experiment I: Inter-space Aligument This first experiment is directly inspired by Abdou al. (2021). In this case, we want to assess the align- ment between color and LM feature spaces. For measuring such alignment, we replicated the set- tings proposed by (Abdou et al., 2021) for the case of (i) Linear Mapping (LMap), where given a set of n (color, color name) pairs, the alignment is mea- sured as the fitness of the regressor W C IR that minimizes |XW YI + all [|], with regularization parameter, X E ReXdLMM the color names embeddings and Y E Rnx3 the vectors coming from the color space, and (ii) Representa- tional Similarity Analysis (RSA) (Kriegeskorte et al., 2008), the non-param can-aarmmetric method, whose score is operationalized via the mean Kendall's between both modalities. In addition, we pro- pose to model alignment an Optimal Transport (OT) (Peyré et al., 2019) problem, where the goal is to find transport matrix that minimizes the cost of moving all text samples onto their corresponding color samples. We rely on Gromov- Wasserstein distance (GW) (Peyré et al., 2016), which extends the OT setting from samples to metric spaces. GW finds mapping T E B that minimizes the GW cost, Eig.tl/ | subject to < Ty < 1, Σ, T = n and Σ, T = // where TEXT = cosl X, X) E Baxn and COLOR cos(Y, (Y, Y) Rnxn are the within- domain similarity matrices. Therefore, T denotes the probability of assigning a sample i in the text space to a sample j in the color space.
For all experiments, we used three language models, namely, BERT (Devlin et al., 2019), Roberta (Liu et al., 2019), T5 (Raffel et al., 2020), all of them in their large type, based their availability HuggingFace*. As con- trol baseline, we use FastText word embeddings (Bojanowski al., 2017). 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 LLaMa, given the size of our intro- duced 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 controlled in-filling setting, limiting what the L.L can output and allowing to parse their generations automatically. More
Language Models:
C CCOLOR 1 T The
'https://huggingfaaa ctt.cco//oooooooo co/models

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Reference
Cumpurative
Targd
bert-arge
Rotien mest ied DARKEK than
Ripe rod papper
fastreat
fastheet
Light fones grermihh
LIGUTER
green
Steechy sweater pink
DUBPER than
Cheved bubblegum pink
Table 3: Examples of color descriptions from COLOR- NAMES and their most suitable comparative (Winn and Muresan, 2018) as obtained by our matching procedure.
smingh
PISTARY
Ver medurieg
(a)
(b)
Figure 2: Alignment using (a) LMap (b) OT.
ative] than description,". The remaining k-th tuple is appended as "descriplion; is [MASK] than description," i.e., the comparative has been masked. The resulting prompt is passed through the LM and the ranking of the most likely tokens for [MASK] is retrieved. As evaluation metric, we chose the Mcan Reciprocal Rank (MRR), as en- codes the position of the correct answer among the raking provided by the LMM. We experimented using a total set of 81 comparatives and varying parameter K from 5 to 20. We performed the same uniform segmentation in terms of subjectivity and conereteness. Results in general showed surpris- ingly good results in terms of MRR, in several cases, LM outputs the correct comparative at posi- tion 1. There was a natural decay in performance when K was smaller, but for K > 10 results were consistent across models and segments. Figure 3 presents the results for K 10, showing uni- formity that led us to speculate that for this task, subjectivity may not be relevant as a control fac- tor. From a qualitative point of view, Figure 4 shows the result of constructing a graph based on the comparative relationships correctly inferred by the LM. As it can be seen, (a) there is color coher- ence in terms of the neighboring relationships, and (b) when sampling paths form the graphs, transi- tions are consistent. Further investigation of these structures is left for future work. Additionally, try- ing assess the impact on the language model selection, we experimented considering ChatGPT (web-based query) and Llama-2 (llama-2-1 2-bbcchhtt as language models tasked to predict the compara- tives. We found that, in several cases (with different prompts) even when using a k-shot setting, these models often created new types of comparatives (c.g. "SIMILAR IN LIGHTNESS"). As such new comparatives are not present in the ground truth of our dataset, our evaluation framework becomes increasingly complicated since we would need further post-procest the model outputs. Such a more complex experiment is left for future work.
We grouped the examples into uniform segments associated to the subjectivity and concreteness scores, computing alignment scores per segment, per LM. In general, results show the alignment is low, and it decreases as subjectivity increases. Sim- ilarly to (Abdou al., 2021), FastText behaves as a strong baseline, we hypothesize that given the uniqueness of the descriptions, its n-gram based tokenizer could be more stable than the tokenizers implemented within the LMs. Figure show the results for all models on all segments using LMap and OT. (We omitted RSA as its behavior is very similar to LMap).
Experiment II: Perceptual Structure via Com- parative Identification The objective of this ex- periment is determine if LM can structure re- lationships between color descriptions without ac- cessing the associated points in color space. To that end we design a task where, given two color descriptions, the LM has to determine the cor- rect comparative that relates both descriptions (e.g. darker or lighter). To this end, we firstly match the dataset provided by Winn and Muresan (2018), which consists of tuples (1 reference color points 1. comparative, [target color points]) against COL- ORNAMES, by sampling (color, color description) pairs (c,, ed;), (cz, ed,) and retrieving the compar- ative that minimizes simultaneously the distance between [reference color points] and and [tar- get color points] and Cj. After this step, we have, for any pair of descriptions in the COLORNAMES dataset, a ranking with the most suitable compara- tives, based on explicit grounding. Table provides matched examples.
We operationalize the task as few shot infer- ence. Firstly, we select randomly from the matched dataset, K tuples of the form (description com- parative, description j ), from which K are used to construct the labeled part of a prompt, following the template "description, is fcompar-

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0.75
fax
A30
banana
taxi
aest
sun
banono
all
Alignment
0.529
0X11
0.737
0.503
0.413
01447
0.43
MRR
0.50
MRR
bert-large
MRR
0.770
0.797
0.881
0.735
0.801
0.693
0.681
roberta-large
0.25
t5-large
Table 4: Results for the sample extracted from the yel- low spectrum of the dataset.
0.00
low medium high Subjectivity
low medium high Concreteness
groups. We selected three distinct groups: group with semantics about taxis, one about bananas and one about sun/sunset/sunriss. We computed the alignment score and the MRR associated to com- parative prediction of each group independently, for pairs of groups and for the combination of all of them, seen in Table 4.
Figure 3: Mean Reciprocal Rank (MRR) for compara- tive inference across language models.
Tivded sundhwos
cituus water
sundyy susi
As we can see, in general the alignment scores are reasonable for single groups, but we start combining them, the scores drop. This is expected as in most cases there is a token that becomes anchor which is slightly modified by the rest of the description. On the other hand, in the prediction of the comparative among pairs descriptions, we can sce that the accuracies (measured with MRR) dropping as we combine the sets, but still remain mostly in the same ballpark. This, while not conclu- sive evidence, helps us approximate to the notion that alignment can indeed be influenced by the se- mantics of the descriptions, but does not seem to play big role how the LM structure the information using comparatives.
grose
(a)
(b)
Figure 4: Examples of perceptual structure obtained by leveraging the correct comparative predictions.
Impact of inner context Finally, differently from (Abdou et al., 2021), our descriptions are not appended any additional context encoding time, want to assess if their complexity acts as a natural source of context. For example, for the description mustard taxi yellow, we can see how the generic yellow color word is being conditioned by two nouns, mustard and taxi. An analysis of our data showed that out of 900K instances, around 390K (43 %) contain a color word. Based on this, split the data into two chunks and re-run the experiments described above. The results show that for the case of the alignment task, the mean R-scores from the regressor associated with the set of descriptions that have and do not have a color word are 0.401 and 0.381 respectively (using BERT- large). We can see that there is in fact a difference, although the ranges are within the results reported. Moreover, given the full set of (color, color de- scription) pairs, we cluster them using the color representations using k-means. From the resulting set of clusters, we choose the one that groups the yellows, as an example. Within that cluster, we performed new grouping, this time using the em- beddings of the color descriptions. From this, now obtained 22 subgroups (again, using standard k-means) that semantically close (inner context) but that are globally constrained by the color spec- trum chosen (yellow). We now study the alignment within conantically-clse groups and in-between
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 compara- tive relationships in new, challenging dataset that encapsulates elements 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 ground- ing. In terms of future work, we 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 scenarios of low resource translation.
A Acknowledgements
We would like to thank the anonymous reviewers for their insightful and constructive feedback.

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Limitations
Edward Gibson, Richard Futrell. Julian Vara-Ettinger, Kyle Mahowald, Leon Bergen, Sivalogeswaran Rat- nasingam, Mitchell Gibson, Steven T Piantadosi, and Bevil R Conway, 2017. Color naming across lan- guages reflects color use. Proceedings of the Na- tional Academy of Sciences, 114(40):10885-1077
One of the key limitations of the current work is its focus solely on English language, which, in terms of color language, naturally compresses the cultural aspects associated to English-speaking so- cieties. This is clear when we analyze in detail the vocabulary, we can find cultural archetypes that are probably not transferable. In that, there is an inherent challenge on how to learn color naming conventions in more broad way. For example, for applications related to low resource languages, understanding the use of language for color de- scription could be helpful for anchoring linguistic patterns.
Yoav Goldberg. 2019. Assessing bert's 's syntactic abili- ties. arXiv preprint
Xudong Han, Philip Schulz, and Trevor Cohn. 2019. Grounding learning of modifier dynamics: An ap- plication to color naming. In Proceedings of the 2019 Conference ral Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-JJCNLP), pages 1488-1493, Hong Kong. China. Association for Computational Linguistics.
Empirical Methods in Natu-
Ethics Statement
Kazuya Kawakami, Chris Dyer, Bryan Routledge, and Noah A Smith. 2016. Character sequence models for colorful words. In Proceedings of the 2016 Con- ference on Empirical Methods in Natural Language Processing, pages 1949-1954.
Our main objective is to understand how language is used to communicate color. This has several applications, for example in e-commerce, such as search, product reviews (where color is an impor- tant attribute). While directly our study tries to abstract from specific user information, it is certain that language usage identification could be used for prospecting or targeting individuals from a specific cultural background.
Nikolaus Kriegeskorte, Marieke Mur, and Peter A Ban- dettini. 2008. Representational similarity analysis- connecting the branches of systems neuroscience. Frontiers in systems neuroscience, page 4.
Delwin T. Lindsey and Angela M. Brown. 2014. The color lexicon of American English. Journal of Vision. 14.
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Reviews:
[{"review_id": "wBC2dD4Qp5", "reviewer": "Reviewer_tgng", "summary": "", "questions": "A: What do you mean by \"expected usage\" in line 181?\n", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer `tgng` for the comprehensive and constructive review. We appreciate the confirmation that they found no reasons for rejection in the context of a short paper contribution, and that the topic is relevant.\nRegarding question A on the word `usage`, we would like to clarify that this was an unfortunate typo. That paragraph aimed at describing Figure 2, where we added three example words, but `Figure 3` was referenced incorrectly. \nWe appreciate the detailed list of typos and mistakes prepared; we will make sure they are fixed for the camera-ready version upon acceptance.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- useful (derived) data resource; cleaned-up subset of ColorNames\n- interesting finding, in the failure to replicate direct alignment, but finding good prediction performance on comparatives\n", "reasons_to_reject": "- none\n", "questions_to_authors": "A: What do you mean by \"expected usage\" in line 181?\n", "paper_topic_and_main_contributions": "This short paper presents a new (derived) data resource on \"naturally occuring\" colour names and uses it in two experiments meant to probe the extent to which perceptual structure is mirrored in conceptual structure induced by (text-only) models. In the first experiment, following earlier work, a direct mapping is induced on the data points between the colour space of the named colours and the representation space for the colour names. Unlike in prior work, the resulting alignment is low, which the authors speculate may be due to the more complex names used in this resource. In the second experiment, the linguistic representation space is probed by predicting relations between points (\"darker as\", \"murkier\", etc.). Here, it is shown that the predictions for a relation word made by the text-only model largely conform with what the perceptual space would predict.\n\n- NLP engineering experiment\n- new data resources\n- replication (experiment I)\n", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- l 001: \"the need for grounding in language understanding is an active research topic\" sounds clunky... \"whether language understanding requires grounding is an active topic\"?\n\n- l 009: I find it a bit misleading to say that you \"collect\" this resource -- you derive a useful dataset from an existing dataset that someone else collected.\n\n- l 011: \"examples , and\"\n\n- l 039: \"colors in general is tractable\" .. sounds odd grammatically, but also not clear what you mean. Tasks can be tractable or not, but colours aren't a task\n\n- l 166: \"be it be using\" --> by using?\n\n- l 255: closing ] is missing\n\n- l 268: would be helpful to denote the prompt also typographically, or at least put it into quotes: \"is appended as \"description is [MASK] than description\", i.e. the ...\"\n\n- l 281: I guess you forgot to poner el numero aca :-)\n"}}, {"review_id": "elNj98cQdb", "reviewer": "Reviewer_H3n7", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the comprehensive review and insightful feedback. Regarding the comments about the usage of larger, more recent LLMs like GPT-3.5, we would like to point out that this is an alternative that we naturally considered. However, several reasons let us to the selection of models that appears in the paper, as follows. \n\n(1) Firstly, the decision of using our selected LLMs responds to the necessity to compare on an apples-to-apples manner to previous work, concretely, to the results of Abdou et al. (2021). Since our alignment experiments are an extension of their work, we were interested in confirming if the alignment reported by them still holds on the new settings we propose (a new dataset).\n\n(2) In the second place, we would like to point out that in order to perform our alignment experiments we require to 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 (~1 million examples), our estimates indicated that embedding efforts would unfortunately lie beyond our budget.\n\n(3) 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.\n\nTo illustrate this issue, we implemented a test workflow based on the reviewer's feedback, considering ChatGPT (web-based query) and llama-2 (llama-2-13b-chat). We found that, for the prediction of the comparative task, in several cases (different prompts) even when using k-shot settings, these models often created new types of comparatives (e.g. \"SIMILAR IN LIGHTNESS\"). As such new comparatives are not present in the ground truth of our dataset, our evaluation framework becomes increasingly complicated since we would need to further post-process the model outputs. Though this lies beyond the scope of our paper, we regard your idea as future work.\n\nWe sincerely hope the reviewer can re-consider the score in light of our comments. We are happy to clarify the rationale behind our model selection and clearly explain the limitations of our current approach.\n\nReferences:\n- Abdou et al. (2021): https://aclanthology.org/2021.conll-1.9.pdf", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This seems to be a good incremental contribution to an important topic, namely exploring the knowledge-related capacities of large language models. The paper is written with great clarity, all metrics are explained in detail.", "reasons_to_reject": "The only criticism I could list here is that no more recent LLM like GPT-3.5 / 4 was used, but this is not the authors' fault\nbut dependent on the lack of availability of the models. Though, I am sure, these models may have performed significantly differently.", "questions_to_authors": "", "paper_topic_and_main_contributions": "Color Language has recently be used to investigate how and what LLMs learn in the pre-training phase.\nFor the purpose of studying this, they collect data from ColorNames, compiling a dataset of really creative and exotic color descriptions.\nThe first task is assessing the inter-space alignment between color and LLM spaces with fine-grained color name segmentation. For the second experiment, they focus on the representation obtained from the LLMs and their ability to match color comparatives with the structure of color descriptions.\nThey build upon the work of Abdou et al. (2021), which means this work is incremental.\n \nResults show that the alignment scores are low, but results on comparative estimations are comparably high. The authors’ conclude that LLMs learn structure through language, but cannot transfer it to knowledge source-related concepts (such as color in this case). \n\nFor experiments, the authors used BERT, Roberta and T5  (all large)\n\nIn detail, the first experiment tries to align the positions of colours in colour space with their prose description in LLMs representational space. FastText embeddings are used as a baseline space.\nResults for this experiment are low, performance is anti-proportional to the subjectivity of the expression.\n\nThe second experiment explores whether LLMs can structure relationships without the information given by the Color space. The task of the LLM is do determine, given two color descriptions, the correct comparative (e.g. whether color y is lighter or darker than y). With consistent results across data points, which are “surprisingly good”, the authors’ conclude that subjectivity is less important in this task.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "l. 152 we make use of\nl. 166 be it be"}, "extra_text": {}}, {"review_id": "eOaTXVyBMS", "reviewer": "Reviewer_T8is", "summary": "", "questions": "I must have missed this - how did you compute embeddings for multiword phrases? Averaged the embeddings of all of the subwords the make up the phrase?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank reviewer `T8is` for their comprehensive review. We appreciate their comments regarding our lack of clarity in terms of the scope and underlying assumption. We agree that things can be improved in this sense, and commit to doing so for the camera-ready version. \n\nRegarding their comments about the validity of comparing sentences such as `NYC Taxi` and `vomit milkshake`, we agree that without providing any additional context, there is little reason to expect that a model or (probably) \na human would consider them as semantically related. Now, we would like to emphasize that our work is constrained to the field of color descriptions (as an instance of studying language grounding). What we are trying to do is to assess if these models, trained with the standard regimes, are able to internally structure perceptual information (in this case, color) that is communicated in terms of text-only input i.e. without explicitly receiving color input. We do so by means of the two experiments described in the paper.\n\nWe think their suggestion of explicitly informing the LLM that we are considering a color description scenario is extremely pertinent and well-aligned with the comment above. Now, if we take a look at how Abdou et al. (2021) dealt with this issue, we can see that since their color names are monolexemic, they were able to match against the feature production norms dataset by McReid et al. (2005), where concepts (usually nouns) are linked to a series of features, one of them being the main color. For example, they have tuples such as (red, apple), (blue, sky). Given the simplicity of this dataset, Abdou et al. (2021) are able to use those tuples to generate template-based contexts such as `[apple] is [red]`. From this, one can clearly see how passing such a sentence to the LM could reinforce the notion that `red` in this context is used as a description. \n\nDifferently from the above scenario, our descriptions are more complex, i.e., there are no canonical objects that have such colors descriptions as attributes, therefore the technique by Abdou et al. (2021) cannot be used. However, as we can see from the nature of our dataset, the richness of the descriptions can itself act as context. For example, for the description `mustard taxi yellow`, we can see how the generic `yellow` color word is being conditioned by two nouns, `mustard` and `taxi`. Note that such description contains the color word `yellow`. We could say that this is approximately what the template-based approach of Abdou et al. (2021) \nAn analysis of our data showed us that out of 900K instances, around 390K (43 %) contain a ``color word''. Based on this, we split the data into two chunks and re-run the experiments from our paper. The results show that for the case of the alignment task, the mean R scores from the regressor associated with the set of descriptions that have and do not have a color word are 0.401 and 0.381 respectively (using BERT-large). We can see that there is in fact a difference, although the ranges are within the results reported in our paper.\n\nMoreover, in order to more comprehensively approximate an answer to your question, we performed one additional experiment. Given the full set of (color, description) pairs $D$, we cluster them using the color representations using k-means. From the resulting set of clusters $C$, we choose one, for example, the one that groups `yellows` $c_{yellow} \\in C$. Within that set of (color, description) pairs, we can now perform a new grouping, this time using the embeddings of the descriptions. With this, we now obtain 22 subgroups (again, using standard k-means) that are semantically close (inner context) but that are globally constrained by the color spectrum chosen (yellow). For example, we see that there is a subgroup that contains most descriptions about taxis :  \n\n| **color (hex)** | **description**        |\n|-----------------|------------------------|\n| #e9c503          | mustard taxi yellow    |\n| #f0d654          | mustard taxi cab       |\n| #b77a0b          | australian taxi yellow |\n| #c59813          | old taxi cab           |\n| #cab65e          | faded taxi bus         |\n| #f2b312          | plain taxi yellow      |\n\nIf the reviewer is kind enough to check the hex colors in the table (can be input directly at https://colornames.org/), they will see that most colors are yellow-ish. We can now study the alignment within semantically-close groups and in-between groups. We selected three distinct groups: a group with semantics about `taxis`, one about `bananas` and one about `sun/sunset/sunrise`. We computed the alignment score of each group independently, for pairs of groups and for the combination of all of them:\n\n\n|           | `taxi` | `sun` | `banana` | `taxi-sun` | `taxi-banana` | `sun-banana` | `all` |\n|-----------|--------|-------|----------|------------|---------------|--------------|-------|\n| alignment | 0.529  | 0.811 | 0.737    | 0.503      | 0.413         | 0.447        | 0.453 |\n\n\nAs we can see, in general the alignment scores are reasonable for single groups, but as we start combining them, the scores drop. This is expected as in most cases there is a token that becomes an anchor which is slightly modified by the rest of the description. On the other hand, if we conduct the prediction of the comparative among pairs of description (second experiment in the original paper), we can see that the accuracies (measured with MRR) dropping as we combine the sets, but still remain mostly in the same ballpark: \n\n\n|     | `taxi` | `sun` | `banana` | `taxi-sun` | `taxi-banana` | `sun-banana` | `all` |\n|-----|--------|-------|----------|------------|---------------|--------------|-------|\n| mrr | 0.770  | 0.797 | 0.881    | 0.735      | 0.801         | 0.693        | 0.681 |\n\nThis, while not conclusive evidence, helps us approximate to the notion that alignment can indeed be influenced by the semantics of the descriptions, but it does not seem to play a big role on how the LM structure the information using comparatives. \n\nFinally, we also consider the reviewer has an important point regarding the need for human signals to provide more consistency to the results. While we consider LMs may provide general agreement, you are also right that depending on human factors, the perception of color can vary. That is a study we consider is currently out of the scope of a short paper, but for sure we consider it to include in future work, acknowledging cultural and demographic differences in color perception and color communication.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The scientific question of how much language models can learn about colors without being exposed to visual information is an interesting one.\n\nThe paper is generally well written.", "reasons_to_reject": "I may be fundamentally misunderstanding something, but the premise of the first experiment seems strange to me. Do we really expect the embedding of \"NYC taxis\" to be as close to the embedding of \"vomit milkshake\" as these colors happen to be close to each other in RGB space? At the risk of stating the obvious - unlike \"yellow\" and \"red\", which are pure color terms, the phrase \"NYC taxis\" is expected to carry a lot of semantic information that has nothing to do with color. Perhaps the experiment would make more sense if there was a way to prompt the model to focus on the colors denoted by those phrases - this is probably how you would set up a human experiment here? Speaking of human experiments, it would be helpful to get a sense of how well humans do on these tasks - not just for the similarity alignment task (where you'd have the person separately give similariy judgments to colors and to phrases like \"vomit milkshare\"), but also for the darkness rating task. I'm not personally sure, for example, that I would rate \"stretchy sweater pink\" as \"deeper\" than \"chewed bubblegum pink\".", "questions_to_authors": "I must have missed this - how did you compute embeddings for multiword phrases? Averaged the embeddings of all of the subwords the make up the phrase?", "paper_topic_and_main_contributions": "This paper contributes to the literature on the alignment between language models' color space and the visual properties of the color (i.e. comparing embedding distance to distance in RGB space). Whereas earlier work examined the embeddings of monoleximic color terms such as \"red\" or \"yellow\", this paper looks at more complex ad-hoc color names from an online database; those include \"NYC taxis\" and \"vomit milkshake\". The experiments reported in the paper found that alignment for these more complex color names was poor, but the model was able to order color terms by darkness, e.g., \"rotten meat red\" is darker than \"ripe red pepper\".", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper analyzes representations of color terms and phrases in text-only models, including the alignment between embedding representations and actual color space, as well as relative representations of two color descriptions. The paper finds that subjectivity and abstractness in color descriptions poses a significant challenge for text-only models. The study is well-motivated and very interesting, but there are two methodological concerns brought by a reviewer. 

First, that there is a possible confound that might be artificially lowering alignment quality -- specifically, that some color names don't imply they are being used as color names at all. The authors perform additional experiments to show that when only considering color names that actually include known color terms (e.g., "yellow"), alignments are slightly higher quality than those without known color terms. I encourage the authors to include the analysis in a final version of the paper.

Second, that while the data here was generated by people annotating color hex codes, there is no verification or calculation of agreement that collected descriptions accurately represent their paired hex codes. While I agree this analysis would be out of scope for a short paper, the lack of such analysis should be acknowledged in the paper limitations.
Decision: Accept-Findings