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REVIEW 3 major objections 6 minor 135 references

CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A new scoring suite measures a text-to-image model's cultural knowledge by how much its output changes as the prompt gains detail, and reports stronger correlation with culture-matched human ratings than existing automated proxies.

desk verdict A genuinely useful cultural-benchmark idea held back by a three-rater gold standard that needs statistical reinforcement before the headline advantage is trusted. read the letter →

arxiv 2506.08071 v1 pith:23E3R3G5 submitted 2025-06-09 cs.CV

Aniket Rege , Zinnia Nie , Mahesh Ramesh , Unmesh Raskar , Zhuoran Yu , Aditya Kusupati , Yong Jae Lee , Ramya Korlakai Vinayak This is my paper · ORCID
classification cs.CV
keywords culturalrepresentativenesstext-to-imagebiasmarginalinformationattributionlong-taildistributionbenchmarkdatasetWikimediaknowledgegraphgenerativeentanglementdiversityscoring
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

CuRe claims that a text-to-image system's cultural representativeness can be read off from how its output changes as the prompt becomes more informative: adding the artifact's category, region, or both. The paper formalizes this as a family of marginal-information-attribution (MIA) scorers built from three standard score types, and reports that these correlate with culture-matched human Likert ratings more strongly than existing proxy scorers, including ground-truth similarity, CLIP-style text alignment, and Vendi-score diversity. The dataset behind the claim is a 300-artifact benchmark built from the Wikimedia knowledge graph with a six-axis, 32-category, 64-country hierarchy. If the claim holds, cultural representativeness becomes cheaply and scalably measurable without ground-truth images or large user studies.

What carries the argument

The machinery is a coarse-to-fine attribute hierarchy (supercategory $s \rightarrow$ category $c \rightarrow$ name $n$, with region $r$) plus a template family $P(a)$ that turns any subset of these attributes into a prompt. The MIA scorers are the load-bearing objects: $\phi_{PS}$ compares the image generated from the artifact's name to the image generated from its category; $\phi_{ITA}$ averages image-text similarity to the name prompt and to a more informative prompt; $\phi_{DIV}$ averages LPIPS over the four generation conditions $\{n\}$, $\{n,c\}$, $\{n,r\}$, $\{n,c,r\}$. Because these scores compare generated images against other generated images, they require no ground-truth image collection, which is what makes the benchmark scalable, and the 300-artifact dataset with 64 countries plus the culture-matched user study supply the gold scores the scorers are validated against.

What would settle it

Re-run the correlation analysis with a substantially larger rater pool, tens of workers per artifact, and a calibrated rubric, then recompute the Spearman values in the paper's tables; if the MIA scorers' advantage over ground-truth-requiring baselines shrinks to within the bootstrap uncertainty of the gold scores, the central claim collapses. A second, cheaper check targets $\phi_{PS}$ directly: for an artifact whose category image $I(c)$ is clearly wrong to culture-matched judges, the scorer will rank the artifact as long-tail even when the model generates it faithfully.

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Extended reading notes

Core claim

The central claim is that the marginal utility of attribute specification is a valid, cheaper proxy for human judgments of cultural representativeness. For an artifact $n$ from category $c$ and region $r$, the paper defines $\phi_{PS}(n) = \mathrm{sim}(I(n), I(c))$, $\phi_{ITA}(a) = \left[\mathrm{sim}(I(n), P(n)) + \mathrm{sim}(I(n), P(a))\right]/2$, and $\phi_{DIV} = \mathrm{LPIPS}\big(\{n\},\{n,c\},\{n,r\},\{n,c,r\}\big)$, interpreting a small divergence across these generation conditions as evidence that the artifact sits in the head of the model's learned distribution. Across several image encoders, vision-language models, and six text-to-image systems, these scorers achieve higher-magnitude Spearman correlations with human Likert scores for cultural representativeness, perceptual similarity, and ground-truth likelihood than baselines that either require real reference images or share pretraining data with the generators. The paper also documents generative entanglement: scores computed with models trained on the same web corpora as a text-to-image system systematically overestimate its cultural quality, an error the MIA scorers are more robust to.

Load-bearing premise

The whole evaluation rests on the gold standard: the ratings of just three paid crowd workers per artifact, collected without a rubric, are assumed to be a stable enough measure of cultural representativeness that correlations against them are meaningful.

Editorial extensions

If this is right

  • Cultural representativeness can be benchmarked automatically and at scale, without curated ground-truth image sets or expensive user studies, since the MIA scorers compare generations across prompt informativeness levels.
  • Existing CLIP-style scores are suspect for systems trained on the same web data: the generative-entanglement result implies leaderboards that rely on them systematically overstate cultural quality.
  • The per-artifact scores expose which specific long-tail artifacts fail (for example banku versus pierogi), enabling targeted data curation rather than coarse aggregate judgments.
  • Because diversity correlates inversely with human judgments, interventions that increase generation diversity should be checked against factuality instead of being assumed to improve cultural representativeness.
  • Multimodal LLM judges, while strong overall, are unreliable on Global South details and still hallucinate culture-specific facts, so they cannot replace the MIA scorers without ground-truth anchoring.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same four-prompt marginal-utility protocol should transfer to non-cultural long-tail domains, such as rare species, niche professions, or regional dialects of visual style; a direct test would be to run the protocol on another tail-heavy benchmark and check whether human ratings again align with the divergence scores.
  • The scorers inherit any bias in the model's category prototype: if the model cannot draw a generic "dumpling" well, $\phi_{PS}$ will misrank its dumpling artifacts, so a practical refinement would weight or condition on the quality of $I(c)$.
  • The gold scores are noisier than the reporting suggests, since workers assign 1 and 5 to the same image and sometimes score high while disavowing the image, so the observed correlation advantages should be re-estimated with larger rater pools and a rubric to see whether the ranking of scorers survives.
  • The geography-as-culture proxy, one country per artifact, is the main limit on what the scores mean; extending the attribute vocabulary to religion, language, or ethnicity is a direct application of the same machinery.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper introduces CuRe, a 300-artifact cultural benchmark dataset built from the Wikimedia knowledge graph with a coarse-to-fine categorical hierarchy, and a family of 'Marginal Information Attribution' (MIA) scorers that measure how a text-to-image system's outputs change as attributes (name, category, region) are added to the prompt. The central claim is that these scorers, instantiated as perceptual similarity (phi_PS), image-text alignment (phi_ITA), and diversity (phi_DIV), correlate better with human Likert judgments of cultural representativeness, perceptual similarity, and ground-truth likelihood than existing proxy scorers. The paper validates this claim through a Prolific user study with three self-identified cultural workers per artifact, reports Spearman correlations across multiple image encoders, vision-language models, and six T2I systems, and additionally evaluates Gemini 2.0 Flash as an MLLM judge. The dataset and code are open-sourced.

Significance. If the central claim is validated, CuRe would be a genuinely useful resource: the dataset is broader in country coverage than most prior cultural benchmarks, the hierarchy enables a new form of fine-grained analysis, and the MIA scorers avoid ground-truth image collection, which is a real practical advantage. The paper is also unusually transparent: it discloses the SD 1.5 failure mode, DALL-E 3 refusals, Gemini hallucinations, and illustrative worker disagreement cases, and it ships open code and data. The concern that the 0.5 offset in Delta_phi_PS is a fitted parameter does not survive inspection: the offset is a constant shift and is rank-invariant, so it cannot affect the reported Spearman correlations. However, the validation target itself is the load-bearing weakness: with three no-rubric raters per artifact and documented scale reversals, the Spearman correlations in Tables 2-4 may be attenuated or reordered, and the reported advantages of MIA scorers over baselines are often small enough that uncertainty quantification is essential before the headline claim can be accepted.

major comments (3)
  1. [Sec. 5.1, Appendix C, Appendix D.2, Tables 2-4] The validation target for every comparative claim in Tables 2-4 is the mean of exactly three Prolific workers per artifact, rating Likert items with no rubric. The only inter-annotator agreement analysis reported (Table 10, Appendix D.2) is for the four-image ranking question Q1b, not for the Likert items phi*_CuRe, phi*_PS, and phi*_GT that are the actual gold labels. Figure 13 documents extreme instability on those Likert items: workers assign 1 and 5 to the same generated image, one worker gives a perfect CuRe score while explicitly stating the image does not depict the artifact (E7 Hardangerbunad), and one worker gives a 1 while calling the image 'pretty accurate' (E4 cowboy hat), suggesting scale reversal. With three ratings per artifact, measurement error attenuates all Spearman rhos and can reorder scorers when the differences are as small as 0.02-0.03 (e.g., Table 3, FLUX.1, phi_ITA({c,r}) = 0.27 vs. sim(I(n),P({c,r})) = 0.25). The paper should report bootstrap confidence intervals for every rho in Tables 2-4, plus rater-level robustness checks such as leave-one-rater-out correlations and an inter-rater reliability measure (e.g., ICC) for the Likert items themselves. This is load-bearing because the entire central claim rests on these correlations.
  2. [Tables 2-4] The Spearman correlations are reported as point estimates without any uncertainty quantification. The best absolute correlations are modest (roughly 0.3-0.5 for phi_PS and phi_ITA, and about -0.3 for phi_DIV on the stronger T2I systems), which means the best scorer explains at most about 25% of the rank variance in the gold labels. Moreover, the claimed advantage over baselines is often small: for example, in Table 3 (FLUX.1 [dev]) phi_ITA({c,r}) is 0.27-0.42 depending on the gold column, while sim(I(n),P({c,r})) is 0.25-0.38 and sim(I(n),P(n)) is 0.24-0.38; in Table 2 the Delta_phi_PS variants sometimes beat phi_GT by only 0.01-0.02. Without confidence intervals or a test of the difference between correlated Spearman coefficients, the phrase 'much stronger correlations' in the abstract and conclusion is not supported. The authors should either add interval estimates and significance tests, or temper the claim to 'consistent but modest improvements.'
  3. [Sec. 5.6, Table 5] The benchmark comparison in Table 5 selects, for each scorer class, 'the variant which had the highest negative Spearman's rho with gold scores on average.' Because the same gold scores were used in Tables 2-4 to identify that best variant, the benchmark rankings in Table 5 are not an out-of-sample evaluation of the scorer family; the variant selection can inflate the apparent performance of the proposed scorers. This selection procedure should be described as such, or better, the choice should be fixed a priori or evaluated with a held-out split of artifacts, so that the benchmark results are not circular with respect to the correlation analysis.
minor comments (6)
  1. [Abstract / Sec. 5.3] The abstract and the conclusion claim 'much stronger correlations' with human judgments, but the magnitudes in Tables 2-4 are moderate and the advantages over strong baselines are often small. Please align the wording with the quantitative results.
  2. [Sec. 5.1, Sec. 5.2, Sec. 5.6, Sec. 4.2] There are several typos and wording slips: 'judments' appears in Secs. 5.2 and 5.3, 'sensistive' in Sec. 5.6, 'inscalable' in Sec. 4.2, and 'Accurate' should likely be 'Accuracy' in Table 1. A careful proofreading pass is needed.
  3. [Table 5] The column headers 'SL2', 'DN2', 'AV2', 'L2B', 'WIT', 'ALX' are not expanded in the table caption; the abbreviations are only explained in surrounding text. Please expand them in the caption for readability.
  4. [Appendix D.2] The worker agreement numbers in Table 10 are reported as mean +/- standard deviation, but the standard deviation is over artifacts, not a confidence interval for the mean agreement. Reporting a confidence interval or the distribution of per-artifact agreement would be more informative.
  5. [Sec. 5.6] The Image Arena ELO scores are stated to be 'taken on September 24, 2025,' which is after the arXiv submission date of June 9, 2025; please clarify the access date or update the reference accordingly.
  6. [Appendix I.2] The regional-trend observation that Gemini failures concentrate in Global South artifacts is interesting and worth stating more prominently, but the supporting evidence is a small set of qualitative examples; consider quantifying this trend across all 300 artifacts.

Circularity Check

0 steps flagged · score 0.0 of 10

CuRe's MIA scorers are fixed, parameter-free definitions validated against external human labels; no circularity found.

full rationale

The central claim is that marginal-information-attribution (MIA) scorers correlate better than existing proxy scorers with human judgments of cultural representativeness, perceptual similarity, and diversity. The MIA scorers are closed-form, parameter-free definitions: phi_PS(a)=sim(I(a),I(c)) (Eq. 2), phi_ITA(a)=[sim(I(n),P(n))+sim(I(n),P(a))]/2 (Eq. 3), and phi_DIV=LPIPS({n},{n,c},{n,r},{n,c,r}) (Eq. 4). No parameter in any of these formulas is fitted to the human Likert scores; the validation in Tables 2-4 is a Spearman correlation between independently collected Prolific judgments (Sec. 5.1) and these fixed scorer outputs. The additive 0.5 in the delta-phi_PS divergence is rank-invariant, so it cannot manufacture Spearman correlation. The 'marginal utility' insight is a hypothesis motivated by qualitative examples, then tested on the full dataset; it is not defined in terms of the gold labels. The only author-overlapping citation, [10], appears in Sec. 5.4 while listing preference-reward-model baselines; it is not load-bearing for the core argument and does not supply an unverified premise or forbid alternatives. The paper's own Limitations section acknowledges validity limits, such as using geography as a proxy for culture and weaker scorer performance on Stable Diffusion 1.5; the 3-rater, no-rubric gold-standard concern flagged by the reader is a measurement-reliability issue, not a circularity issue, because the scorers would still be well-defined even if the human labels were noisy. No self-definitional reduction, fitted-input-called-prediction, imported uniqueness theorem, ansatz-by-citation, or renaming of a known result was found; the derivation chain is self-contained against external human judgments.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central MIA definitions themselves are transparent and parameter-free; the load-bearing extras are dataset construction, gold-standard reliability, and the choice of Spearman correlation. No new physical or formal entities are introduced.

free parameters (3)
  • Minimum ground-truth image threshold = 4
    Wikimedia country/category entries with fewer than four images are excluded to form the ground-truth set (Sec. 3, Appendix B), shaping which artifacts enter the benchmark.
  • Seed counts per prompt = 20 (SDXL, SD1.5), 4 (others), 80 for P(c)
    Used to average scorer values; chosen by compute constraints, affects the variance of all reported scores (Appendix A.1).
  • Delta phi_PS offset = 0.5
    Added to make divergence scores comparable in scale; it is rank-invariant and does not change Spearman correlations, but it is an ad hoc constant (Sec. 5.3).
assumptions (5)
  • domain assumption Wikimedia category graph and the authors' manual shortlist of six cultural axes and 32 categories provide a valid operationalization of cultural artifacts and regions.
    The dataset is the substrate for every score and every human survey; Sec. 3 and Appendix B describe the selection.
  • domain assumption Geography, encoded as a single country of origin per artifact, is an adequate proxy for culture.
    The authors state this as a limitation; it affects worker matching and how region-specific scores are aggregated.
  • domain assumption Three Prolific workers per artifact, matched by country of nationality, produce reliable gold-standard Likert scores.
    All correlations are computed against these scores; Fig. 13 documents large worker disagreements and miscalibration.
  • standard math Spearman rank correlation against gold scores is the right measure of scorer quality.
    Used throughout Sec. 5.2-5.5; it ignores calibration and absolute error, so a scorer can rank well yet be systematically biased.
  • domain assumption The four prompt templates in Table 6 instantiate the attributes {n,c,r} equivalently across all artifacts.
    All scorer comparisons assume differences between scores come from the model's cultural knowledge rather than from prompt wording.

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Cite this review

Pith. "Pith review of CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems." pith.science (2026). https://pith.science/paper/23E3R3G5

@misc{pith2026250608071,
  author       = {Pith},
  title        = {Pith review of: CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/23E3R3G5}},
  note         = {Machine review of arXiv:2506.08071}
}
read the original abstract

Popular text-to-image (T2I) systems are trained on web-scraped data, which is heavily Amero and Euro-centric, underrepresenting the cultures of the Global South. To analyze these biases, we introduce CuRe, a novel and scalable benchmarking and scoring suite for cultural representativeness that leverages the marginal utility of attribute specification to T2I systems as a proxy for human judgments. Our CuRe benchmark dataset has a novel categorical hierarchy built from the crowdsourced Wikimedia knowledge graph, with 300 cultural artifacts across 32 cultural subcategories grouped into six broad cultural axes (food, art, fashion, architecture, celebrations, and people). Our dataset's categorical hierarchy enables CuRe scorers to evaluate T2I systems by analyzing their response to increasing the informativeness of text conditioning, enabling fine-grained cultural comparisons. We empirically observe much stronger correlations of our class of scorers to human judgments of perceptual similarity, image-text alignment, and cultural diversity across image encoders (SigLIP 2, AIMV2 and DINOv2), vision-language models (OpenCLIP, SigLIP 2, Gemini 2.0 Flash) and state-of-the-art text-to-image systems, including three variants of Stable Diffusion (1.5, XL, 3.5 Large), FLUX.1 [dev], Ideogram 2.0, and DALL-E 3. The code and dataset is open-sourced and available at https://aniketrege.github.io/cure/.

Figures

Figures reproduced from arXiv: 2506.08071 by the authors.

Figure 1
Figure 1. Three images of types of pottery generated by a state [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The CuRe dataset is constructed in a hierarchical fash￾ion (s → c → n, r) from Wikimedia with 300 cultural artifacts n ∈ N grouped into 6 cultural axes s ∈ S, 32 cultural categories c ∈ C, described by a name n and associated region r. For exam￾ple, s = “food”, c = “dumpling”, n = “banku”, r = “Ghana”. tion of objects, people, and language. These dataset biases propagate to T2I systems after training. Text-to-Image … view at source ↗
Figure 3
Figure 3. An overview of the scorers and user study of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: There is a large drop in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Computation of image-text alignment score [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: A qualitative comparisons of our proposed MIA scorers compared to baselines for three scorer classes: a) Perceptual Similarity [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Computation of diversity score ϕDIV on n = “modak”, a type of dumpling from India. ϕDIV computes an aggre￾gate pairwise dissimilarity (LPIPS [76]) between multiple seeds of images generated with varying attribute specification, i.e. I(n), I(n, c), I(n, r), I(n, c, r). …
Figure 9
Figure 9. Figure 9: Q1b: Querying users to rank (order) the similarity of the ground truth images to the AI generated image from highest (top) to lowest (bottom). Research Study on the Cultural Biases of Gener￾ative AI Thank you for taking the time for this survey. We are a team of resear…
Figure 8
Figure 8. Figure 8: Q1a: Querying users for perceptual similarity of T2I system generated image to ground-truth images. through Prolific, a large crowdsourcing tool. For each ar￾tifact n in the CuRe dataset, we hire three workers whose country of nationality match the region r of the arti…
Figure 10
Figure 10. Figure 10: User study interface for CuRe. Finally, the user is also asked to share a textual descrip￾tion of the specific details contributing to the accuracy or in￾accuracy of the T2I system output in Q3c. This fine-grained information is useful to identify details of why and h…
Figure 12
Figure 12. Figure 12: Demographic information questions [PITH_FULL_IMAGE:figures/full_fig_p020_12.png]
Figure 13
Figure 13. Figure 13: Feedbacks of the workers in cases of high disagreement over perceptions of cultural representativeness [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 13
Figure 13. Figure 13: Feedbacks of the workers in cases of high disagreement over perceptions of cultural representativeness [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]
Figure 14
Figure 14. Figure 14: Responses to “Do you identify with the culture of [country]?” summed across all surveys belonging to each region [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: A histogram of worker familiarity with generative AI models like ChatGPT or DALL-E from 1 (“Never Heard of It”) to 5 [PITH_FULL_IMAGE:figures/full_fig_p024_15.png]
Figure 16
Figure 16. Figure 16: Frequency estimation (log scale) for 50 cultural artifacts across each of the six supercategories (cultural axes) in the CuRe dataset on LAION-SD15, the fully open pretraining dataset of Stable Diffusion 1.5 [50]. E. Concept Frequency Estimation In recent times, state…
Figure 17
Figure 17. Figure 17: Scatter plots of Left: ϕGT (a) and Right: ϕP S(a) for 50 artifacts from all 6 supercategories in the CuRe dataset generated using FLUX.1 [dev]. Each data point represents a single cultural artifact, and is an average of the scores for each T2I generated seed. All imag…
Figure 18
Figure 18. Figure 18: Visualization of quantitative perceptual similarity (PS) scorers and user study [PITH_FULL_IMAGE:figures/full_fig_p032_18.png]
Figure 19
Figure 19. Figure 19: Visualization of quantitative Image-Text Alignment (ITA) scorers, user study [PITH_FULL_IMAGE:figures/full_fig_p034_19.png]
Figure 20
Figure 20. Figure 20: A bar plot of \Delta ϕDIV across all artifacts in the CuRe dataset aggregated at the supercategory level (mean + std error bar) for each T2I system in our evaluation set. We separate each bar at the supercategory level into artifacts belonging to the Global North (GN)…
Figure 21
Figure 21. Figure 21: Diversity scorer and user feedback 37 [PITH_FULL_IMAGE:figures/full_fig_p037_21.png]
Figure 22
Figure 22. Figure 22: Visualization of Gemini 2.0 Flash responses where the MLLM either failed or succeeded in identifying nuances compared to [PITH_FULL_IMAGE:figures/full_fig_p040_22.png]
Figure 22
Figure 22. Figure 22: Continued from previous page. E# AI Image Real Image Feedback E5 Artifact: Takht-e Fulad Category:Monument/Memorial ϕ ∗CuRe : 3.67 Region: Iran Super Category: Architecture ϕGemini : 1 Human Judgments (User Study): 1. so the image has certain specification like carvin…

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    ϕ∗CuRe= 1.3/5 Khanuja et al

    I feel like the AI focused too much on the word Mansion, and especially the american/older british kind. ϕ∗CuRe= 1.3/5 Khanuja et al. [31]= 0.040 Ventura et al. [65]= 0.053 ϕIT A(c, r) = 0.096 E5 Zw¨olf Glaubensartikel

  99. [107]

    Overall the vegitation seems possible to find in my country

  100. [108]

    ϕ∗CuRe= 4.7/5 Khanuja et al

    This image could be a shot from above of some forest in my country. ϕ∗CuRe= 4.7/5 Khanuja et al. [31]= 0.108 Ventura et al. [65]= 0.091 ϕIT A(c, r) = 0.079 E6 Michelle Bachelet

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    She’s a well known Chilean politician, so a lot of people recognize her face

    The image doesn’t have the exact facial features of Michelle Bachelet. She’s a well known Chilean politician, so a lot of people recognize her face. The image looks too fat and the hair is wrong

  102. [110]

    Banku” artifact in Fig. 3). Intuitively, this evaluates how much diversity changes when we mix images of “Banku

    Its the features of the face, they are not completly wrong, but together they make a face who cant be taken to be Bachelet, also the hair looks really fake. ϕ∗CuRe= 2.0/5 Khanuja et al. [31]= 0.066 Ventura et al. [65]= 0.054 ϕIT A(c, r) = 0.164 Figure 19. Visualization of quan...

  103. [111]

    Although, the wear and tear as well as the shape of the statue being mummy-like would be found in my country

    This image has used a different facial feature of the statue you would not see in my country of culture. Although, the wear and tear as well as the shape of the statue being mummy-like would be found in my country

  104. [112]

    The material it is made of and the type of cloth it seems to be wearing

    The similarities lies in the cat structure generally. The material it is made of and the type of cloth it seems to be wearing. The part where it is different from my culture is that the eyes are looking at the side which is very unlikely. Moreover, the smile itself is so not t...

  105. [113]

    May need to add shrimps

    Should have less liquid sauce. May need to add shrimps. The noodle should be fried

  106. [114]

    Each individual strand can be traced which seems unlikely in a real noodle image ϕ∗CuRe= 4/5 LP IP S(n) = 0.70 ϕDIV= 0.59 E3 Third Mainland Bridge

    The noodles in the AI image looks weird and too smooth/plump compared to real noodle dishes. Each individual strand can be traced which seems unlikely in a real noodle image ϕ∗CuRe= 4/5 LP IP S(n) = 0.70 ϕDIV= 0.59 E3 Third Mainland Bridge

  107. [115]

    The image contains the distinctive part of the real location and image, it contains most of the details of the real thing

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    The image shows more or fewer lanes than the actual four lanes

    The inaccurate details is The Number of Lanes. The image shows more or fewer lanes than the actual four lanes. The image inaccurately represents the bridge structural elements such. ϕ∗CuRe= 4.5/5 LP IP S(n) = 0.50 ϕDIV= 0.50 E4 Rabat Lighthouse

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    The catholic or christian cross

  110. [118]

    I think gen AI did a good job here, but missed some details like the big square in front of the lighthouse and the window’s sizes are somewhat big here ϕ∗CuRe= 5/5 LP IP S(n) = 0.51 ϕDIV= 0.54 E5 Penelope Cruz

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    The facial features are very similar to the real ones

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    The white clothes is typical mediterranean

    The hair is typical of any country, so it could as well be spanish. The white clothes is typical mediterranean. ϕ∗CuRe= 4/5 LP IP S(n) = 0.74 ϕDIV= 0.62 E6 Vaso de los Guerrero

  113. [121]

    The AI-generated image looks too unreal; it doesn’t feel natural, as if it was too exaggerated

  114. [122]

    The decorations are in relief and not painted

    The image has floral filigrees and not warriors. The decorations are in relief and not painted. In addition, the vessel is lacquered with gloss ϕ∗CuRe= 2/5 LP IP S(n) = 0.75 ϕDIV= 0.55 Figure 21. Diversity scorer and user feedback 37 I. MLLM as a Judge To our knowledge for the...

  115. [123]

    On a scale of 1-5, how similar do you think the generated image on the left is to the real images on the right? Use the following scale: 1: Not at all similar 2: Slightly similar 3: Reasonably Similar 4: Very Similar 5: Extremely Similar

  116. [124]

    Not at all similar

    Provide a brief explanation for your similarity rating, detailing specific visual aspects you com- pared. Format your response strictly as JSON with the keys ”similarity rating” (for the number 1-5) and ”simi- larity explanation” (for the text explanation). Example Response fr...

  117. [125]

    Cultural Representativeness: On a scale of 1- 5 (where 1 is ”Highly Unlikely” and 5 is ”Ex- tremely Likely”), how likely can this item be found in {country}?

  118. [126]

    Ground-Truth Likelihood: On a scale of 1-5 (where 1 is ”Highly Unlikely” and 5 is ”Extremely Likely”), how likely is this an accurate image of {item name}?

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    AI Image

    Description: What specific details in the AI- generated image make it accurate or inaccurate compared to how this object typically appears in {country} or your understanding of {item name}? Format your response as JSON with keys: ’country likelihood’, ’item accuracy’, and ’de-...

  120. [128]

    The image seems inaccurate from the images of Kuti I used to see.3

    The image is someone holding a microphone but that is not femi kuti2. The image seems inaccurate from the images of Kuti I used to see.3. The face is not just right with with me and the hair is just off....Femi has very distinctive blend of grey hair. MLLM Judgment (Gemini 2.0...

  121. [129]

    The rice and the colour of the rice after been cooked makes it look like jollof from my culture.3

    The image gets the color of Jollof rice2. The rice and the colour of the rice after been cooked makes it look like jollof from my culture.3. The image has used the correct kind of rice, ingredients, and sauce, in my country MLLM Judgment (Gemini 2.0 Flash): The item can almost...

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    It’d be like, let’s say we are trying to find certain colored horses, but perhaps AIgenerates the horses with patterns of color never found in nature.2

    I feel like it’s close to getting it right, but the patterns on the waist and sleeves, for example, makes it look like almostwe’d never see something like this. It’d be like, let’s say we are trying to find certain colored horses, but perhaps AIgenerates the horses with patter...

  123. [131]

    The image has used an outfit that other indigenous people from northern hemisphere might use, but based on myexperience it doesn’t look extremely similar to Finnish Sami headwear

    The person wearing the hat doesn’t look Northern European.2. The image has used an outfit that other indigenous people from northern hemisphere might use, but based on myexperience it doesn’t look extremely similar to Finnish Sami headwear. The fabric texture and colors are di...

  124. [132]

    The coloring is different and Isfahanian buildings have different sets of colors and style more colorful.3

    so the image has certain specification like carvings are quite similar, the stone types also similar to the one I sawin my country, however I think this image is closer to Takhte-Jamshid, as it does not have blue ceramic which usuallybe used in Esfahan an is one of the icons o...

  125. [133]

    The beard needsimprovement as each era in Iranian culture, men used to have different hair and beard styles

    The only thing similar to Amir Kabir AI to the real life is the facial structures like nose and eyes. The beard needsimprovement as each era in Iranian culture, men used to have different hair and beard styles. The cone shaped hat isimportant in displaying who this person was ...

  126. [134]

    The image shown was a type of an animal which is obviously so far from ”Kiping” which is a type of food.3

    The image does not portray the meaning of Kiping at all.2. The image shown was a type of an animal which is obviously so far from ”Kiping” which is a type of food.3. The image is an animal holding a spray bottle, it has no connection to kiping. MLLM Judgment (Gemini 2.0 Flash)...

  127. [135]

    The image is very similar to the name of Pulseira.3

    This is exactly how a pulseira is, a piece of jewellery that goes on the wrist2. The image is very similar to the name of Pulseira.3. This bracelet represents one of the forms of objects that we wear on our wrist, very beautiful with details in shiny stones. MLLM Judgment (Gem...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.