Venue: NAACL 2025

# Culture-TRIP: Culturally-Aware Text-to-Image Generation

with Iterative Prompt Refinement

Suchae Jeong\({}^{1}\), Inseong Choi\({}^{1}\) , Youngsik Yun\({}^{2}\), Jihie Kim\({}^{2}\)+,

\({}^{1}\)Department of Computer Science and Engineering, Dongguk University,

\({}^{2}\)Department of Computer Science and Artificial Intelligence, Dongguk University

sys3606@dgu.ac.kr, jihie.kim@dgu.edu

###### Abstract

Text-to-Image models, including Stable Diffusion, have significantly improved in generating images that are highly semantically aligned with the given prompts. However, existing models may fail to produce appropriate images for the cultural concepts or objects that are not well known or underrepresented in western cultures, such as 'hangari' (Korean utensil). In this paper, we propose a novel approach, **Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement (Culture-TRIP)**, which refines the prompt in order to improve the alignment of the image with such culture nouns in text-to-image models. Our approach (1) retrieves cultural contexts and visual details related to the culture nouns in the prompt and (2) iteratively refines and evaluates the prompt based on a set of cultural criteria and large language models. The refinement process utilizes the information retrieved from Wikipedia and the Web. Our user survey, conducted with 66 participants from eight different countries demonstrates that our proposed approach enhances the alignment between the images and the prompts. In particular, C-TRIP demonstrates improved alignment between the generated images and underrepresented culture nouns. Resource can be found at [https://shane3606.github.io/Culture-TRIP](https://shane3606.github.io/Culture-TRIP).

## 1 Introduction

To date, many Text-to-Image Models Ramesh et al. (2022); Rombach et al. (2022); Ruiz et al. (2023) have demonstrated remarkable improvements. Despite the outstanding performance in the text-to-image models, the models fail to align the images with the culture nouns in the prompts, such as 'ao dai'(a Vietnamese clothing) or 'hangari' (a Korean utensil). Most of these issues stem from the large training datasets gathered by crawling the Internet without paying attention to the details of the cultural elements Yun and Kim (2024). Furthermore, Internet access varies significantly across countries Birhane et al. (2023); Luccioni et al. (2024), leading to challenges in appropriately aligning culture nouns due to insufficient data, as shown in Figure 1 (b).

Representation is important in AI applications. Appropriate representation can positively affect viewers, while inappropriate ones can negatively affect them and can even be harmful Castaneda (2018). Culture nouns are essential elements that often represent the identity and the uniqueness of the given culture. The misrepresentation of culture nouns by existing text-to-image models may cause dissatisfaction in the corresponding countries. Moreover, the models may reinforce harmful stereotypes about particular cultures.

In this paper, we introduce a new approach that generates more culturally aligned images for the given culture nouns, called **Culturally-Aware Text-to-Image Generation with Iterative Prompt**

Figure 1: Comparison between Stable Diffusion with and without our proposed approach, C-TRIP. (a) shows an image of a _hangari_ from Wikipedia. (b) is an image generated by Stable Diffusion 2, while (c) shows an image generated with our approach. The additional knowledge about _hangari_ (highlighted in red) in (c) helps the model generate an image that more closely resembles the actual _hangari_.





**Refinement (C-TRIP).** C-TRIP focuses on refining the prompt to ensure that the culture nouns are appropriately represented in the generated images, as shown in Figure 1 (c). Our goal is to generate culturally-aware images by appropriately aligning culture nouns with images from underrepresented countries.

Our research question is, _How can we refine the prompt so that text-to-image models generate images that appropriately align with culture nouns?_ The Culture Capsules Taylor and Sorensen (1961) is an educational approach designed to help learners who have not directly experienced a culture gain the proper understanding. This approach explains cultural contexts and visual details, enabling learners to gain comprehensive understand unfamiliar cultures. Through this process, learners can develop a deeper profound awareness of different cultures.

Inspired by the Culture Capsules, C-TRIP first retrieves cultural contexts and visual details related to the culture nouns in the prompt and then iteratively refines and evaluates the prompt against the criteria used in culture education. Large Language Models (LLMs) are used for this iterative refinement and evaluation process, guiding the text-to-image model in generating images for culture nouns.

We conducted experiments across eight countries, refining a total of 10,000 prompts by using 50 prompt templates for each of the 25 culture nouns per country. To evaluate our results, we recruited participants who were a native of the corresponding country and had a high familiarity with the culture to rank images generated by Stable Diffusion 2 Rombach et al. (2022), with or without the proposed iterative prompt refinement. The 66 participants across eight countries evaluated the 990 generated images, and C-TRIP received ratings for cultural alignment that were 18.84% on average higher than the baseline's. In particular, our approach demonstrated more improvement for relatively the Unrecognized/Underrepresented Culture nouns (_UC nouns_ for short) than for the Recognized/Common Culture nouns (_RC nouns_ for short).

Our contributions are as follows:

1. We introduce culturally-aware image generation with a prompt that improves the representation of 'culture nouns'--cultural concepts or objects often overlooked by existing text-to-image models.
2. We propose a novel approach, **C-TRIP (Culturally-Aware Text-to-Image GeneRation with Iterative Prompt Refinement), which iteratively refines the prompt in order to improve the alignment of culture nouns in the images generated by text-to-image models.
3. Human evaluations by representatives across eight countries demonstrate that our refined prompts enhance the alignment between generated images and culture nouns. In particular, C-TRIP demonstrated more improvement for UC nouns than RC nouns.

## 2 Related Work

### Cultural Text-to-Image Generation

Previously, various methods have been proposed to address the cultural bias in text-to-image models Cho et al. (2023); Friedrich et al. (2023); Luciconi et al. (2024). Liu et al. (2024) collected a cultural dataset called CCUB across nine cultural categories for five countries and proposed a training technique named ScoFT to address cultural bias. Similarly, Kannen et al. (2024) collected the CUBE across three cultural categories for eight countries. However, these approaches are highly resource-intensive, demanding significant time and cost for data collection.

Other works Basu et al. (2023); Bansal et al. (2022) attempted to mitigate cultural bias by modifying prompt. However, merely adding contextual information such as country names to prompt is proven insufficient in mitigating cultural bias, particularly for the concepts from underrepresented countries.

Unlike the previous approaches, our approach refines prompt based on cultural information and visual details to improve the alignment of text-to-image models with culture nouns, which are significant for representing unique concepts and objects across cultures.

### Prompt Engineering in Text-to-Image Generation

The prompt as input to the text-to-image generation guides the images created by the models. Many studies Oppenlaender (2023); Liu and Chilton (2022); Brade et al. (2023) on prompt engineering aimed at optimizing user-desired images by text-to-image models

Wen et al. (2024) propose a method for learning hard prompts that is optimized for text-to-image generation. This approach demonstrates how to generate prompt with minimal tokens that effectively guide the model to produce images in a specific style. Yao et al. (2024) propose a refinement method that aligns input prompt with training prompts, ensuring that text-to-image models produces high-quality images.

Unlike the existing approaches, we refine the prompt based on cultural information related to culture nouns, guiding text-to-image models to generate images that align appropriately with cultural representation.

### Refinement

Refinement is a method designed to improve output quality through feedback and the application of a refiner. Learned Refiners Schick et al. (2022); Saunders et al. (2022) involve providing feedback through a trained refiner, which requires human-annotated data for the training process. In contrast, Prompted Refiners Peng et al. (2023); Yang et al. (2022) offer feedback through prompting without the trained refiner. Recently, a method called self-refine Madaan et al. (2024) has been proposed, which performs iterative feedback and refinement using a single Large Language Model (LLM) without external supervision.

Unlike the self-refine methods designed for LLM tasks, we iteratively refine the prompt based on cultural contexts and visual details to enhance the understanding of culture nouns in text-to-image models.

## 3 Method

Inspired by the Culture Capsules Taylor and Sorensen (1961), an educational method that introduces unfamiliar cultures through cultural contexts and visual details, our method refines the prompt for text-to-image models by incorporating cultural contexts and visual details relevant to the culture noun. In order to include only information essential for cultural expressions in the image, external information is retrieved, and an iterative refinement and evaluation process is conducted based on the criteria derived from both cultural contexts and visual details.

Our overall architecture is depicted in Figure 2. We first retrieve cultural information from Wikipedia and Web content (Section 3.1), then second iteratively refine and evaluate the prompt based on scores (Section 3.2) until the stop condition is satisfied.

### Cultural Information Retrieval

In obtaining raw information related to culture nouns, we use two sources. Given a culture noun, we first retrieve the Wikipedia content in order to leverage cultural contexts and visual details. Second, for certain culture nouns that lack sufficient information on Wikipedia, we perform additional retrieval from the Web. By making use of both Wikipedia and the Web, we can collect sufficient raw data, even for relatively uncommon UC nouns.

### Iterative Prompt Refinement

The iterative refining process consists of 3 key steps: _Refine_, _Scoring_, and _Feedback_. The _Refine_ step refines the raw information using the prompt

Figure 2: C-TRIP Overview. First, retrieve cultural contexts (cultural background, purpose) and visual details related to the culture nouns as described in Section 3.1. Then, refining the prompt based on the obtained information. We iteratively evaluate and refine the prompt as described in Section 3.2.

 

and feedback from the previous _Feedback_ step. The _Scoring_ step evaluates the refined prompt based on five cultural criteria. Finally, the _Feedback_ step proposes revisions in the prompt based on the refined prompt and the evaluation score. All three steps were implemented using LLaMA-3-70B (Dubey et al., 2024), as iterative refinement processes benefit from larger LLM (Madaan et al., 2024) with the latest open-source model.

RefineThe retrieved raw information \(I\) for the culture noun \(K\) is used to refine the prompts in the _Refine_ step. In this step, the refined prompt _RP_ is typically generated based on feedback \(F\). For the first step, only the raw information \(I\) and the prompt \(P\) are used. In the equations, \(\parallel\) denotes concatenation throughout the paper.

\[{}^{R}{P}_{i}=\begin{cases}{Refine}(K||I||P)&if:i=0,\\ {Refine}(K||I||RP_{i-1}||F_{i-1})&if:j>0.\end{cases} \tag{1}\]

Through the _Refine_ step, only the information essential for cultural education is extracted from the raw data. The final refined prompt guides the text-to-image model in generating images for culture nouns.

ScoringIn the _Scoring_ step, the refined prompts are evaluated based on five cultural criteria: Clarity, Background, Purpose, Visual Elements, and Comparable Objects. If the total score does exceed the threshold (thresh.) or a specified maximum iteration \(i\), the process stops; otherwise, it proceeds to the _Feedback_ step for further refinement.

\[score_{i}=Scoring(K||RP_{i}) \tag{2}\]

The five scoring criteria are structured based on the Culture Capsules (Taylor and Sorensen, 1961), which organize the criteria into two primary aspects: cultural contexts and visual details. Specifically, _Clarity_, _Background_, and _Purpose_ are categorized as cultural contexts, while _Visual Elements_ and _Comparable Objects_ fall under visual details. Detailed descriptions of each evaluation criterion are provided in Table 1.

FeedbackIn the _Feedback_ step, each score is reviewed based on the criteria, and feedback is provided along with suggestions to improve the scores.

\[{}^{F}_{i}=Feedback(K||RP_{i}||score_{i}) \tag{3}\]

Details illustrating the evolution of prompts during the refinement process, along with templates for each step, can be found in Appendix C

## 4 Experiments

### Data Preparation

Culture NounsThe culture nouns are phonetically transcribed into English based on the original pronunciation in their respective languages (e.g., hangari, pronounced/hzngoarri/). However, when an English equivalent exists, they are represented in

\begin{table}
\begin{tabular}{c c p{85.4pt}} \hline \hline Aspect & Criterion & Description \\ \hline  & **Clarity** & The overall clarity of the information, specifically whether the necessary details to explain the culture noun are clearly and easily conveyed. \\ \cline{2-3} Cultural Contexts & **Background** & Whether the prompt provides appropriate historical or temporal context. \\ \cline{2-3}  & **Purpose** & Whether the prompt describes the purpose or usage of the culture noun. \\ \hline  & **Visual Elements** & Whether sufficient visual information, such as color and shape, is provided. \\ \cline{2-3} Visual Details & **Comparable Objects** & Whether the prompt offers a well-known or famous example by drawing a comparison to the culture nouns. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Criteria for scoring refined information. C-TRIP performs scoring of the refined prompt based on five criteria across the aspects of cultural contexts and visual details. Cultural contexts are a criterion for evaluating cultural information, and visual details are criteria for assessing visual information relevant to image generation. Each criterion is assigned a score ranging from 0 to 10.

 

the format of 'Adjective form for the country + English expression' (e.g., Korean pagoda) to signify culture nouns.

SetupBased on Basu et al. (2023), which addresses cultural bias in text-to-image generation by modifying prompts, we selected eight countries representing diverse cultural backgrounds: India, Pakistan, China, Japan, South Korea, Vietnam, the United States, and Germany. Drawing on research by Liu et al. (2024) on culturally-aware text-to-image models, we focused on eight specific categories that typically represent culture in visual expressions: architecture, city landmarks, clothing, dance & music, visual arts, food & drink, religion & festivals, and utensils and tools. We generated 10,000 prompts using 50 prompt templates for each of the 25 culture nouns per country. These templates, generated by GPT-4o, incorporate culture nouns into typical scenarios, enabling consistent prompt generation for experimentation without relying on real-world prompts.

### Baselines for Ablation Study

To effectively analyze the specific contributions of cultural contexts and visual details based on the Culture Capsules approach, the baseline and ablation configurations were established in order to systematically examine the roles of cultural contexts (Clarity, Background, Purpose) and visual details (Visual Elements, Comparable Objects).

In this study, we evaluated three configurations of the C-TRIP, each with different criteria for prompt refinement: C-TRIP\({}_{0}\), C-TRIP\({}_{3}\), and C-TRIP\({}_{5}\). For C-TRIP\({}_{3}\) and C-TRIP\({}_{5}\), the refinement process was performed up to a maximum of 5 iterations, ensuring that the prompts reached the desired level of cultural and visual alignment.

These configurations were applied to 10,000 Base Prompts, resulting in 40,000 refined prompts. Subsequently, 80,000 images were generated, with two images created for each prompt using Stable Diffusion 2Rombach et al. (2022b).

C-TRIP\({}_{0}\)C-TRIP\({}_{0}\) utilizes prompts augmented with raw cultural information without applying the _Iterative Prompt Refinement_. This configuration is used to evaluate the baseline effect of unrefined cultural information, enabling an assessment of how much alignment can be achieved without iterative refinement.

C-TRIP\({}_{3}\)C-TRIP\({}_{3}\) refines the prompts based solely on the cultural context criteria (Clarity, Background, and Purpose). This setup evaluates the contribution of cultural context alone without incorporating visual details. This enables assessing how effectively the refined cultural information enhances alignment with the intended culture nouns in the generated images.

C-TRIP\({}_{5}\)C-TRIP\({}_{5}\) incorporates both cultural context and visual details, refining prompts according to all five criteria: Clarity, Background, Purpose, Visual Elements, and Comparable Objects. This configuration assesses whether adding visual details improves the alignment. By comparing C-TRIP\({}_{3}\) and C-TRIP\({}_{5}\), we can evaluate how much visual details enhance cultural alignment in the generated images.

### Evaluation

User Survey.The alignment of images with culture nouns is inherently subjective and can only be appropriately evaluated by participants of the respective cultural groups based on country. Accordingly, surveys were distributed to individuals who were either native to the respective countries or had at least three years of cultural experience to evaluate our approach. Participants were provided with survey questions based on their chosen country. Each survey page presented 4 images of a randomly selected culture noun. Each survey page contains 4 evaluation questions to rank images: (a) Cultural Representation, (b) The Naturalness of the Keyword, (c) Offensiveness, and (d) Description and Image Alignment. Participants were asked to evaluate a randomly ordered set of images for each question. The image ranked first was considered the most appropriately represented and least offensive, while the image ranked fourth was deemed the most inappropriate and offensive. Detailed information regarding the user survey is provided in the appendix E.

We employed the Matrix Mean-Subsequence Reduced (MMSR) model Ma and Olshevsky (2020), an established algorithm Majdi and Rodriguez (2023) for noise label aggregation provided by crowd-kit Ostalov et al. (2021), to quantitatively estimate subjective performance perception. Using MMSR, the labels from all respondents were aggregated through weighted majority voting based on the assessment of their reliability. Subsequently, the MMSR+Vote method was applied, in which 

labels were further aggregated and ranked using simple majority voting.

Automatic Evaluation.In addition to the cultural survey, we measured the VIEScore [20] using GPT-4o, demonstrating a strong correlation with human evaluations of text-guide image generation. The VIEScore assesses the Semantic Consistency (SC) and the Perceptual Quality (PQ) and Overall score based on these metrics. To evaluate C-TRIP, 150 Base Prompts were randomly sampled from each country. These prompts were then processed through C-TRIP configurations, resulting in a total of 600 samples per country for which scores were measured.

## 5 Results

Qualitative ComparisonFigure 3 compares the images generated from each C-TRIP configuration and the Base Prompt described in Section 4.2. The refined prompt generated by C-TRIP provides cultural knowledge to Stable Diffusion 2, contributing to the generation of culturally-aware images. C-TRIP\({}_{5}\), which includes the visual details criteria, demonstrated higher quality representation. That is, Stable Diffusion 2 can produce better images when the prompts include appropriate cultural contexts and visual details. With these enhancements, C-TRIP effectively improves the model's ability to generate culturally relevant images.

Figure 3: Qualitative comparison of C-TRIP ablated configurations compared to Base Prompt. The six columns can be divided into two groups: Relatively UC nouns (left four columns) and RC nouns (right two columns). The left group needed C-TRIP to introduce culture nouns that were underrepresented in Text-to-Image models, while the right group had to recall what they already knew through the additional information provided.

 

User Survey Results.A total of 66 participants from eight countries participated in the survey, ranking the four configurations of the generated images based on the four evaluation questions specified in the survey. The average ranking for each selected configuration is presented in the Table 2.

When applying the MMSR algorithm to all the survey responses, C-TRIP\({}_{5}\) achieved the highest ranking overall. C-TRIP\({}_{3}\) ranked second-highest, using cultural contexts criteria alone is still effective in the refinement. However, C-TRIP\({}_{0}\) scored lower than the Base Prompt, particularly in the Description and Image Alignment questions. This suggests that unrefined prompts may introduce irrelevant details, which can degrade description quality.

Further analyzing alignment, we converted the rankings into binary comparisons between C-TRIP\({}_{5}\) and the Base Prompt, isolating and comparing their respective rankings. This approach revealed that C-TRIP\({}_{5}\) demonstrated higher average alignment in 61% of the time.

In conclusion, incorporating both cultural contexts and, importantly, visual details in the scoring process significantly enhances the alignment of culture nouns with the generated images.

Automatic Evaluation Results.C-TRIP\({}_{5}\) scored the best in the evaluation of the SC score, evaluating the semantic similarity between the prompt and the generated image. However, C-TRIP\({}_{0}\) scored the best in the PQ score, which assesses the naturalness of the generated image. This result suggests that additional iterative refinement can potentially reduce the perceived naturalness of the image. It appears that the additional refinement process may have overemphasized specific details, thereby deviating from the naturalness in quality as perceived by GPT-4o. Nevertheless, C-TRIP\({}_{5}\) still outperformed the Base Prompt by 3.4%. Overall, C-TRIP\({}_{5}\) maintained the highest score. In conclusion, as in the user survey results, incorporating both cultural and visual refinements consistently enhances the overall performance.

## 6 Ablation Study for UC Nouns

The Culture Capsule approach is a method that teaches learners who have not experienced a particular culture. With this idea in mind, we aimed to apply a similar approach to words that Stable Diffusion 2 is not familiar with. In this section, we analyzed and compared the Unrecognized/Underrepresented Culture nouns (UC nouns) and the Common/Recognized Culture nouns (RC nouns).

UC and RC noun groupsWe categorized culture nouns into UC and RC noun groups according to their frequency within the training dataset used for Stable Diffusion 2. To achieve this, we analyzed Re-LAION-2B-en-research, a filtered true subset of the LAION-2B-en Schuhmann et al. (2022), using a \(p_{unsafe}>0.95\) threshold and keyword-based filters to remove potentially suspicious content.1

Footnote 1: [https://laion.ai/blog/relaion-5b/](https://laion.ai/blog/relaion-5b/)

Our analysis focused on culture nouns within the dataset captions, classifying them based on their frequency of appearance. These nouns were then grouped using a quartile-based approach (Q1 to Q4), with Q1 and Q2 representing UC nouns and Q3 and Q4 representing RC nouns. This grouping provided a structured means to evaluate the C-TRIP's capacity to generate culturally aligned images across varying levels of representation.

User Survey.We used the normalized improvement score to evaluate UC nouns. This score is calculated by normalizing the difference between the

\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & Evaluation Criteria & Base Prompt & C-TRIP\({}_{0}\) & C-TRIP\({}_{3}\) & C-TRIP\({}_{5}\) \\ \hline \multirow{7}{*}{User Survey (\(\downarrow\))} & Cultural Representation & 2.73 & 2.53 & 2.56 & **2.18** \\  & The Naturalness of the Keyword & 2.76 & 2.69 & 2.38 & **2.18** \\  & Offensiveness & 2.81 & 2.60 & 2.46 & **2.13** \\  & Description and Image Alignment & 2.53 & 2.66 & 2.51 & **2.30** \\ \hline \multirow{4}{*}{VIEScore (\(\uparrow\))} & Semantic Consistency (SC) & 0.37 & 0.36 & 0.37 & **0.38** \\  & Perceptual Quality (PQ) & 7.50 & **7.81** & 7.80 & 7.76 \\ \cline{1-1}  & Overall Score & 0.71 & 0.71 & 0.73 & **0.74** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of User Survey and VIEScore. The results highlighted in bold indicate the best outcomes. For the User Survey, a lower average rank is better, while for the VIEScore, a higher score is preferred. Except for the Perceptual Quality in the VIEScore, C-TRIP\({}_{5}\) received the best evaluation in all other evaluations.

 

average rankings of C-TRIP\({}_{5}\) and the Base Prompt in the user survey, measuring the improvement of C-TRIP\({}_{5}\) over the Base Prompt. A normalized improvement score higher than 0.45 indicates that C-TRIP's guidelines improved image alignment in Stable Diffusion 2.

Our approach performed the best with the Q1 group, with the highest median score and a narrow IQR, presenting consistent improvement across this group, as shown in Figure 4. This suggests that C-TRIP effectively reinforces the generation of images for the Q1 group, enhancing alignment for UC nouns. The Q2 group demonstrated the highest upper range within the IQR and the second-highest median, suggesting effective but slightly variable improvements. However, the Q3 group showed the lowest performance with a wide variance, which may potentially indicate that additional information could decrease alignment for culture nouns.

Our approach demonstrated an 11.05% higher mean normalized improvement score for UC nouns compared to RC nouns. The t-test yielded a t-statistic of 2.951 and a p-value of 0.0033, providing statistical evidence of a significant performance improvement for the UC noun group. These results indicate that C-TRIP's guidance was more effective in improving alignment with UC nouns than with RC nouns.

Automatic Evaluation.As shown in Table 3, C-TRIP\({}_{5}\) demonstrated improvements in all scores: SC, PQ, and Overall score. The table highlights significant gains in Q1, with notable increases across all scores. However, C-TRIP\({}_{5}\) scored lower in the SC score with Q2 and Q3. This discrepancy with the user survey highlights the potential limitations of using VIEScore, which relies on LLMs trained on culturally biased web-based data. It emphasizes the need for surveys conducted by members of the respective cultural groups when evaluating culture nouns. The consistently low SC scores suggest that LLMs may struggle to accurately assist in recognizing and aligning culture nouns, further limiting VIEScore's effectiveness in assessing culturally specific content.

## 7 Conclusion

In this paper, we introduced **C-TRIP ( Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement)**, a novel approach that iteratively refines prompts to improve the alignment of culture nouns with images generated by existing text-to-image models without any fine-tuning. Experiments across eight countries demonstrated that C-TRIP significantly improves the alignment of culture nouns in generated images, particularly for underrepresented UC nouns. User surveys and automatic evaluations consistently present C-TRIP's superior performance in cultural representation and the semantic consistency.

## 8 Limitations

Sources like Wikipedia and general Web content contain cultural biases [16, 1], which can affect the refinement process and C-TRIP's capacity to provide balanced cultural representation. Future work should focus on enhancing the information retrieval process through developing culturally di

Figure 4: A box plot illustrating the normalized improvement scores for each group (Q1, Q2, Q3, and Q4). A score exceeding 0.45 signifies that the C-TRIP’s guidelines enhance the image alignment of the Stable Diffusion 2 model. Notably, the Q1 group exhibits the highest performance improvement compared to the other groups.

 

verse datasets, thereby ensuring high-quality, relevant data for effective prompt refinement.

The limited scope of prompts utilized in our experiments and human evaluations presents a current limitation and suggests an important avenue for future research. Additionally, our work is constrained by the perceptual biases of human annotators from eight countries. To improve the reliability of evaluation outcomes, future work will emphasize the inclusion of annotators from a broader range of cultural backgrounds.

AcknowledgementsThis research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2025-2020-0-01789), the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2025-RS-2023-00254592) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation), and the Hyundai Motor Chung Mong-Koo Foundation.

## References

* Baeza-Yates [2018] Ricardo Baeza-Yates. 2018. Bias on the web. _Communications of the ACM_, 61(6):54-61.
* Bansal et al. [2022] Hritik Bansal, Da Yin, Masoud Monajatipoor, and Kai-Wei Chang. 2022. How well can text-to-image generative models understand ethical natural language interventions? _arXiv preprint arXiv:2210.15230_.
* Basu et al. [2023] Abhipsa Basu, R Venkatesh Babu, and Danish Pruthi. 2023. Inspecting the geographical representativeness of images from text-to-image models. In _Proceedings of the IEEE/CVF International Conference on Computer Vision_, pages 5136-5147.
* Birhane et al. [2023] Abeba Birhane, Vinay Prabhu, Sang Han, and Vishnu Naresh Boddeti. 2023. On hate scaling laws for data-swamps. _arXiv preprint arXiv:2306.13141_.
* Brade et al. [2023] Stephen Brade, Bryan Wang, Mauricio Sousa, Sageev Oore, and Tovic Grossman. 2023. Promptify: Text-to-image generation through interactive prompt exploration with large language models. In _Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology_, pages 1-14.
* Castaneda [2018] Mari Castaneda. 2018. The power of (mis) representation: Why racial and ethnic stereotypes in the media matter. _Challenging inequalities: Readings in race, ethnicity, and immigration_.
* Cho et al. [2023] Jaemin Cho, Abhay Zala, and Mohit Bansal. 2023. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In _Proceedings of the IEEE/CVF International Conference on Computer Vision_, pages 3043-3054.
* Dubey et al. [2024] Abhimanyu Dubey, Abhinav Jauhr, Abhinav Pandey, Abhishe Kadian, Ahmad Al-Dahale, Aiesha Lettman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. _arXiv preprint arXiv:2407.21783_.
* Friedrich et al. [2023] Felix Friedrich, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Patrick Schramowski, Sasha Luccioni, and Kristian Kersting. 2023. Fair diffusion: Constructing text-to-image generation models on fairness. _arXiv preprint arXiv:2302.10893_.
* Kannen et al. [2024] Nithish Kannen, Arif Ahmad, Marco Andreetto, Vinodukumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, and Shachi Dave. 2024. Beyond aesthetics: Cultural competence in text-to-image models. _arXiv preprint arXiv:2407.06863_.
* Ku et al. [2023] Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue, and Wenhu Chen. 2023. Viesccore: Towards explainable metrics for conditional image synthesis evaluation. _arXiv preprint arXiv:2312.14867_.
* Liu and Chulton [2022] Vivian Liu and Lydia B Chulton. 2022. Design guidelines for prompt engineering text-to-image generative models. In _Proceedings of the 2022 CHI conference on human factors in computing systems_, pages 1-23.
* Liu et al. [2024] Zhixuan Liu, Peter Schladenbrand, Beverley-Claire Okogwu, Wenxuan Peng, Youngssik Yun, Andrew Hundt, Jihie Kim, and Jean Oh. 2024. Scott: Self-contrastive fine-tuning for equitable image generation. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_, pages 10822-10832.
* Luccioni et al. [2024] Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. 2024. Stable bias: Evaluating societal representations in diffusion models. _Advances in Neural Information Processing Systems_, 36.
* Ma and Olshevsky [2020] Qianqian Ma and Alex Olshevsky. 2020. Adversarial crowdsourcing through robust rank-one matrix completion. _Advances in Neural Information Processing Systems_, 33:21841-21852.
* Madaan et al. [2024] Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Halliman, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2024. Self-refine: Iterative refinement with self-feedback. _Advances in Neural Information Processing Systems_, 36.
* Majdi and Rodriguez [2023] Mohammad S Majdi and Jeffrey J Rodriguez. 2023. Crowd-certain: Label aggregation in crowdsourced and ensemble learning classification. _arXiv preprint arXiv:2310.16293_.
* Miquel-Ribe and Laniado [2018] Marc Miquel-Ribe and David Laniado. 2018. Wikipedia culture gap: quantifying content imbalances across 40 language editions. _Frontiers in physics_, 6:54.
* Oppenlaender [2023] Jonas Oppenlaender. 2023. A taxonomy of prompt modifiers for text-to-image generation. _Behaviour & Information Technology_, pages 1-14 

* Peng et al. (2023) Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023. Check your facts and try again: Improving large language models with external knowledge and automated feedback. _arXiv preprint arXiv:2302.12813_.
* Ramesh et al. (2022) Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. _arXiv preprint arXiv:2204.06125_.
* Rombach et al. (2022a) Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. 2022a. High-resolution image synthesis with latent diffusion models. In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_, pages 10684-10695.
* Rombach et al. (2022b) Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. 2022b. High-resolution image synthesis with latent diffusion models. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_, pages 10684-10695.
* Ruiz et al. (2023) Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In _Proceedings of the IEEE/CVF conference on computer vision and pattern recognition_, pages 22500-22510.
* Saunders et al. (2022) William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022. Self-critiquing models for assisting human evaluators. _arXiv preprint arXiv:2206.05802_.
* Schick et al. (2022) Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmparantis, Edouard Grave, and Sebastian Riedel. 2022. Peer: A collaborative language model. _arXiv preprint arXiv:2208.11663_.
* Schaumann et al. (2022) Christoph Schaumann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. 2022. Laion-5b: An open large-scale dataset for training next generation imagenetropolis. _Advances in Neural Information Processing Systems_, 35:25278-25294.
* Taylor and Sorensen (1961) H Darrel Taylor and John L Sorensen. 1961. Culture capsules. _The Modern Language Journal_, 45(8):350-354.
* Ustalov et al. (2021) Dmitry Ustalov, Nikita Pavlichenko, and Boris Tseitlin. 2021. Learning from crowds with crowd-kit. _arXiv preprint arXiv:2109.08584_.
* Wen et al. (2024) Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2024. Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery. _Advances in Neural Information Processing Systems_, 36.
* Yang et al. (2022) Kevin Yang, Yuandong Tian, Nanyun Peng, and Dan Klein. 2022. Re3: Generating longer stories with recursive repromtping and revision. _arXiv preprint arXiv:2210.06774_.
* Yao et al. (2024) Junyi Yao, Yijiang Liu, Zhen Dong, Mingfei Guo, Helan Hu, Kurt Keutzer, Li Du, Daqual Zhou, and Shanghang Zhang. 2024. Promptotic: Align prompt distribution via adapted chain-of-thought. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_, pages 7027-7037.
* Yun and Kim (2024) Youngsik Yun and Jihie Kim. 2024. Cic: A framework for culturally-aware image captioning. In _Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24_, pages 1625-1633. International Joint Conferences on Artificial Intelligence Organization. Main Track.

## Appendix A List of Culture Nouns

This part lists culture nouns for the eight countries used in the experiment. The culture nouns are divided into 8 categories: architecture (3), city & landmark (5), clothing (4), dance & music (2), visual arts (1), food & drink (5), religion & festival (3), and utensils & tools (2). The numbers in parentheses indicate the quantity of nouns used in each category. A total of 25 nouns were extracted and used for each country as shown in Table 6 to 13.

## Appendix B Retrieve Cultural Information

This part covers the process of retrieving cultural information. As mentioned in the text, the retrieval is carried out in two steps. First, information about the culture noun is retrieved from Wikipedia. However, for culture nouns categorized under UC nouns, when information is unavailable or insufficient on Wikipedia, further information is retrieved from the web. Both retrieval processes utilized LangChain modules, with WikipediaQueryRun2 used for Wikipedia searches and GoogleSearchAPIWrapper3 used for web searches. The two examples below show cases where the retrieval was successful through Wikipedia as shown in Figure 12 and where the information was not insufficient through Wikipedia, so additional retrieval was also conducted from the web as shown in 13.

Footnote 2: [https://python.langchain.com/api_reference/community/tools/langchain_community.tools.wikipediaQueveryRun.html](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.wikipediaQueveryRun.html)

Footnote 3: [https://python.langchain.com/docs/integrations/tools/google_search/](https://python.langchain.com/docs/integrations/tools/google_search/) 

First, the prompt is refined, and the score is measured based on the five evaluation criteria defined in Table 1. Next, the feedback process generates an explanation for the given score, based on the refined prompt and the measured score. Finally, the prompt is iteratively refined and scored based on the feedback. The Iterative Prompt Refinement process terminates when the total score of the refined prompt exceeds 40 points or after 5 iterations. The Refine, Score, and Feedback processes all utilize LLaMA-3-70B, and the respective templates can be found in Figure 9, Figure 10, and Figure 11.

## Appendix D Qualitative Comparison Prompts

This part includes the prompts used to generate the images in Figure 3 of the main text. Cultural contexts are written in orange, and visual details are written in cyan. For culture nouns categorized under the UC nouns (dengchi, jade art, and masala dabba), we observe that Stable Diffusion 2 effectively aligns with the culture nouns using prompt refined through our approach.

In contrast, for culture nouns categorized under the RC nouns (Willis Tower and pho), there is no significant difference in the images generated between the refined prompt and the base prompt. For pho (Vietnamese cuisine), Stable Diffusion 2 generates images similar to the original reference image, even without visual details in the refined prompt.

## Appendix E User Survey

To evaluate the performance of our approach, we recruited 66 participants with at least 3 years or older of cultural experience in each of the 8 countries. Even so, to address the mitigate bias further, we recruited at least 5 participants from each country. Table 4 provides detailed information about the participants. Each participant responded to 15 survey pages. A single page of the survey form includes culture nouns, a base prompt, and one image generated from each of the three approaches described in Section 4.2, for a total of four images. Each survey page has a total of four survey items (see Table 15) to rank relative to (a) Cultural Representation, (b) The naturalness of the keyword, (c) Offensiveness, and (d) Description and Image Alignment. A sample of a survey page can be viewed in Figure 15. Survey participants were compensated for their time and contributions in accordance with ethical guidelines.

## Appendix F Additional Qualitative Samples

In Figure 16 to 23, we present additional qualitative examples illustrating how the C_TRIP approach improves the alignment of culture nouns in UC and RC nouns with the generated images by Stable Diffusion 2. Results are showcased across China, Germany, India, Japan, Pakistan, South Korea, USA, and Vietnam. Additionally, our approach effectively enhanced the alignment between prompt containing culture nouns from the UC nouns and the generated images.

## Appendix G Culture noun Distribution by Quartile Group for Each Country in the Re-LAION Dataset

Table 16 presents the culture noun counts for each country, divided into quartiles. Each quartile represents 25% of the data, helping to better understand and compare the frequency of culture nouns across countries. This allows for an analysis of the characteristics of cultural expression distribution by country, making it easier to identify the proportion of UC and RC nouns.

Furthermore, Figures 5 to 8 analyze the distribution of culture nouns across each quartile group. Looking at the top three countries in each group, for Q1, the leading countries were Germany, China, and Vietnam. For Q2, Japan, Vietnam, and India ranked highest. In Q3, China, India, and the USA were the top countries, while in Q4, the USA accounted for more than half, showing a dominant proportion 



\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline  & \multicolumn{3}{c|}{Age} & \multicolumn{2}{c|}{Gender} \\ \cline{2-7}  & 21:30 & 31-40 & 41-50 & Male & Female & Total \\ \hline China & 6 & 1 & 0 & 6 & 1 & 7 \\ \hline Germany & 12 & 0 & 0 & 4 & 8 & 12 \\ \hline India & 5 & 2 & 0 & 3 & 4 & 7 \\ \hline Japan & 6 & 0 & 0 & 2 & 4 & 6 \\ \hline Pakistan & 9 & 3 & 0 & 9 & 3 & 12 \\ \hline South Korea & 9 & 1 & 2 & 9 & 3 & 12 \\ \hline USA & 6 & 0 & 0 & 3 & 3 & 6 \\ \hline Vietnam & 4 & 1 & 0 & 1 & 4 & 5 \\ \hline \end{tabular}
\end{table}
Table 4: This table presents participant information, including age and gender distribution for each culture group.

Figure 5: Country Distribution in Q1 group.

Figure 8: Country Distribution in Q4 group.





\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & Tulou, Siheyuan, Chinese pagoda \\ \hline city \& landmark & Dunhuang (Mogao Caves), Xi’an (Terracotta Army), Beijing (Forbidden City), Beijing (Temple of Heaven), Shanghai (Oriental Pearl Tower) \\ \hline clothing & Tangzhuang, Zhongshan suit, Qipao, Hanfu \\ \hline dance \& music & Lion dance, Yangge \\ \hline visual arts & Jade art \\ \hline food \& drink & Mooncake, Peking duck, Wonton, Xiaolongbao, Chinese hot pot \\ \hline religion \& festival & Chinese New Year, Chinese Mid-Autumn Festival, Qingming \\ \hline utensils \& tools & Chinese wok, Chinese bamboo steamer \\ \hline \end{tabular}
\end{table}
Table 6: Categorization of culture nouns associated with Chinese culture.

\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & German Romanesque, German Gothic, German Baroque \\ \hline city \& landmark & Berlin (Brandenburger Tor), Munich (Munich’s Marienplatz), Cologne (Cologne Cathedral), Bavaria (Neuschwanstein Castle), Heidelberg (Heidelberg Castle) \\ \hline clothing & Lederhosen, Tracht, Dirndl, Schürze \\ \hline dance \& music & Zwiefacher, Schuhplattler \\ \hline visual arts & Deutsch Renaissance \\ \hline food \& drink & Mulled wine, Currywurst, Knodel, Bratwrust, Sauerkraut \\ \hline religion \& festival & Nikolaustag, Weihnachtsmarke, Oktoberfest \\ \hline utensils \& tools & Bratpfanne, Rouladenklammer \\ \hline \end{tabular}
\end{table}
Table 7: Categorization of culture nouns associated with German culture.

\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & Indian stupa, Mughal architecture, Gupta architecture \\ \hline city \& landmark & Agra (Taj Mahal), Delhi (Red Fort), Jaipur (Amber Fort), Allahabad (Allahabad Fort), Delhi (Qutub Minar) \\ \hline clothing & Lehenga, Sari, Shalwar kameez, Dhoti \\ \hline dance \& music & Manipuri dance, Bharantnatyam \\ \hline visual arts & Mughal painting \\ \hline food \& drink & Paratha, Biryani, Vada Pav, Aloo Gobi, Saag \\ \hline religion \& festival & Diwali, Holi, Durga Puja \\ \hline utensils \& tools & Tawa, Masala dabba \\ \hline  & \multicolumn{1}{p{142.3pt}}{} \\ \end{tabular}
\end{table}
Table 8: Categorization of culture nouns associated with Indian culture.





\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & Pakistani Buddhist architecture, Pakistani Indo-Islamic architecture, Pakistani Mughal architecture \\ \hline city \& landmark & Lahore (Badshahi Mosque), Karachi (Mazar-e-Quaid), Islamabad (Faisal Mosquito), Multan (Shrine of Bahauddin Zakariya), Hyderabad (Pakka Qila) \\ \hline clothing & Sherwani, Gharara, Shalwar Kameez, Lehenga dance \& music \\ \hline  & Khattak dance, Jhumanir visual arts & Truck art \\ \hline food \& drink & Biryani, Nihari, Gulab Jamun, Kleer, Gol Gappa religion \& festival \\ \hline religion \& festival & Vaisakhi, Eid al-Fitr, Eid al-Adha utensils \& tools \\ \hline \end{tabular}
\end{table}
Table 10: Categorization of culture nouns associated with Pakistani culture.

\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & Hanok, Korean pagoda, Korean temple \\ \hline city \& landmark & Jeonju (Hanok Village), Seoul (Gyeongbokgung), Gyeongju (Bulguksa), Suwon (Hwascong Fortress), Seoul (Jongmyo Shirine) \\ \hline clothing & Hanbek, Jeongori, Durumagi, Dangui dance \& music Cheoyongmu, Buchaechum visual arts & Minhwa food \& drink Bingsu, Kimchi, Sundubujigae, Bibimbap, Tteokbookki religion \& festival Chuseok, Seollal, Korean New Year utensils \& tools Gamasot, Hangari \\ \hline \end{tabular}
\end{table}
Table 11: Categorization of culture nouns associated with Korean culture.

\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & Shinto architecture, Torii, Echo architecture \\ \hline city \& landmark & Tokyo (Tokyo Tower), Shizuoka (Mount Fuji), Kyoto (Kinkaku-ji), Osaka (Osaka Castle), Matsumoto (Matsumoto Castle) \\ \hline clothing & Hanten, Haori, Hakama, Yukata \\ \hline dance \& music & Kabuki, Noh mai visual arts \\ \hline visual arts & Origami \\ \hline food \& drink & Okonomiyaki, Tempura, Wagashi, Gyudon, Gpyoza religion \& festival \\ \hline religion \& festival & Sapporo Snow Festival, Gion Matsuri, Sanda Matsuri utensils \& tools \\ \hline utensils \& tools & Sashimi böchó, Takoyaki Pan \\ \hline \end{tabular}
\end{table}
Table 9: Categorization of culture nouns associated with Japanese culture.





\begin{table}
\begin{tabular}{l|p{142.3pt}} \hline Category & Culture Nouns \\ \hline \hline architecture & Vietnamese dynasty architecture, Vietnamese still house, Vietnamese pagoda \\ \hline city \& landmark & Hanoi (One Pillar Pagoda), Hanoi (Temple of Literature), Hanoi (Old Quarter), Hue (Imperial City of Hue), Quang Nam (My Son Sanctuary) \\ \hline clothing & Ao dai, Ao ba ba, Ao tu than, Non la \\ \hline dance \& music & Mua lan, Quan ho \\ \hline visual arts & Vietnamese silk painting \\ \hline food \& drink & Banh mi, Goi cuon, Pho, Bun cha, Banh xeo \\ \hline religion \& festival & Vietnamese Lunar New Year, Vietnamese Mid-Autumn Festival, Hung Kings Temple Festival \\ \hline utensils \& tools & Vietnamese wok, Vietnamese clay pot \\ \hline \end{tabular}
\end{table}
Table 13: Categorization of culture nouns associated with Vietnamese culture.

Figure 9: Prompt provided to LLaMA-3-70B in the Refine step. There are two key points in the Refine step. The first is to effectively refine the INFORMATION and incorporate it into the Base prompt. To address the first key point, we structured the first and second paragraphs. The second is to inject the information while maintaining the structure of the Base prompt. At this stage, we also imposed a length limit to prevent exceeding Stable Diffusion’s input limit and to ensure that the Base prompt remains the focal point, avoiding distraction from too much information. For the second key point, we structured the third paragraph.



Figure 12: Example of sufficient information retrieved from Wikipedia: Cowboy hat (USA clothes). This culture noun, classified within the Q4 group and categorized under RC nouns, provided detailed information on its appearance and cultural significance, accessible solely through Wikipedia.

Figure 10: Prompt provided to LLaMA-3-70B in the Scoring step. We provided LLaMA-3-70B with the criteria and definitions set in Table 1 to score each criterion up to 10 points, for a total maximum score of 50 points.

Figure 13: Example of insufficient information retrieved from Wikipedia: Degchi (Pakistan utensil). This culture noun, classified within the Q1 group and categorized under UC nouns, could not be found on Wikipedia. Additional cultural information was subsequently obtained through a web search.

 

* [15] M. C. R. A. Krizhevsky, and D. J. Dean. The classification of deep convolutional neural networks for image recognition. In _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pages 237-240, 2016.
* [16] S. L. Bermanet, P. W. Fowlkes, and T. Darrell. Deep residual learning for large scale visual data. In _Advances in Neural Information Processing Systems (IJCNN)_, pages 89-92, 2016.
* [17] G. H. Chan, Y. Bengio, and E. Rabinovich. Learning representations by jointly embedding vectors. In _NIPS Workshop on Machine Learning_, volume 1, pages 1-10, 2016.
* [18] I. Goodfellow, N. Mirza, J. Warde, and Z. Courville. Generative adversarial nets. In _Advances in neural information processing systems_, pages 135-140, 2016.

[MISSING_PAGE_POST]

 

C-TRIP\({}_{5}\) The gallery opening was a major event for jade art enthusiasts, who gathered to marvel at the exquisite carvings, jewelry, and ornaments on display, crafted from nephrite and jackite minerals prized for their vibrant colors and high translucency. The exhibit featured pieces with delicate filigree patterns, ornate designs, and symbols of good fortune, showcasing the great skill and attention to detail involved in shaping these minerals into beautiful works of art. The event was a testament to the enduring appeal of jade art.

\begin{tabular}{r l l l} \hline  & & Base prompt & hanbok are scattered on the floor after a long day. \\ \cline{3-4} Korea & Hanbok & C-TRIP\({}_{0}\) & hanbok are scattered on the floor after a long day, their delicate fabrics crumpled and dishevaled. Despite the chaos, the vibrant colors and intricate patterns of the garments remain striking, a testament to the beauty and elegance of traditional Korean clothing. \\ \cline{2-4} Korea & Hanbok & C-TRIP\({}_{3}\) & hanbok are scattered on the floor after a long day of festivities and celebrations. The handbox is a traditional Korean dress that is worn for formal or semi-formal occasions. In modern times, Koreans often wear banbok to cultural events and festivals. \\ \cline{2-4}  & C-TRIP\({}_{5}\) & Hanbok are scattered on the floor after a long day, their vibrant colors and intricate designs reflecting Korea's rich cultural heritage. The traditional clothing features a unique blend of straight and curved lines that are representative of a distinctly Korean aesthetic. With its roots tracing back to the Three Kingdoms of Korea period, these elegant dresses are typically worn on special occasions such as weddings and festivals. \\ \hline India & Masala dabba & Case prompt & A simple masala dabba helped achieve the desired texture. \\ \cline{2-4} India & Masala dabba & Case-TRIP\({}_{0}\) & A simple masala dabba helped achieve the desired texture by providing quick access to the necessary spices without having to search through cabinets or drawers. The box’s compact design made it easy to store on the countertop, keeping the workspace clutter-free and organized. With its small spoon, the cook could measure out the perfect amount of each spice. \\ \cline{2-4}  & C-TRIP\({}_{3}\) & A simple masala dabba helped achieve the desired texture, by providing quick access to spices like cumin, coriander, and turmeric, which are essential for many Indian dishes. \\ \cline{1-1} \cline{2-4}  & C-TRIP\({}_{3}\) & A simple masala dabba helped achieve the desired texture, by providing quick access to spices like cumin, coriander, and turmeric, which are essential for many Indian dishes. \\ \cline{1-1} \cline{2-4} Korea & Hanbok & C-TRIP\({}_{0}\) & An example of this paper is given in Table 6.

 

C-TRIP\({}_{5}\) & A simple masala dabba helped achieve the desired texture, its carefully arranged cups holding the secrets to the recipe's success. The cook knew that with this container, they could confidently combine the various spices and seasonings, resulting in a culinary masterpiece. It was an indispensable item in their kitchen, one that had been passed down through generations.

USA & Willis tower & Base prompt & Chicago (wills tower) was beautifully lit up at night. C-TRIP\({}_{0}\) & Chicago (willis tower) was beautifully lit up at night, making it a stunning sight in the city's skyline. Its unique design and impressive height make it a popular spot for photographers and tourists alike. The building's facade is made of anodized aluminum and black glass, which adds to its visual appeal. C-TRIP\({}_{3}\) & Chicago (Willis Tower) was beautifully lit up at night, making it one of Chicago's most popular tourist destinations, with its facade made of anodized aluminum and black glass shining brightly. C-TRIP\({}_{5}\) & The Willis Tower was beautifully lit up at night, its anodized aluminum and black glass facade shimmering against the city lights. The building's unique design took on a new level of sophistication in the evening hours, becoming a beacon of light in the Chicago skyline. Its nighttime beauty was a sight to behold.

Vietnam & Pho & Base prompt & pho is a delicious way to start any meal. & Pho is a delicious way to start any meal, with its warm, comforting broth and tender noodles providing a satisfying and filling experience. C-TRIP\({}_{3}\) & Pho is a delicious way to start any meal, with its warm, comforting broth and soft rice noodles providing a soothing contrast to the crunch of fresh herbs and the savory flavor of meat. C-TRIP\({}_{5}\) & pho is a delicious way to start any meal, providing comfort and sustenance, whether as a light breakfast, a satisfying lunch, or a nourishing dinner.

 



\begin{table}
\begin{tabular}{l l} \hline
**Item Title** & **Item Text** \\ \hline \hline Cultural Representation & Rank the images from 1 for the best representation of [country] culture to 4 for the worst cultural representation. Please ignore image artifacts (such as distorted faces, hands, or glitches) when considering Cultural Representation. **(1=most representative, 4=least representative)** \\ \hline The naturalness of the keyword & Keyword: [keyword], Rank the images from 1 for the most natural-looking to 4 for the least natural-looking. Please ignore image artifacts (such as distorted faces, hands, or glitches) when considering The naturalness of the keyword. **(1=most natural, 4=least natural)** \\ \hline Offensiveness & Rank the images by their offensiveness to you personally, numbering from 1 as the least offensive to 4 for the most offensive. Please ignore image artifacts (such as distorted faces, hands, or glitches) when considering Offensiveness. **(1=least offensive, 4=most offensive)** \\ \hline Description and Image Alignment & Image Description: [prompt], Rank the accuracy of the match between the image and its description. Number each image from 1 for the image best described by the description to 4 for the worst match between image and description. **(1=best described, 4=worst described)** \\ \hline \end{tabular}
\end{table}
Table 15: The four survey items for our human evaluation survey.





**Consent to Participate in a Research Study about Image Generators and South Korea You are being asked to participate in a research study using conducted by the Machine learning lab for Google University. Participation is voluntary. The purpose of this study is to understand users to better represent outside the AI-generated images. Any person and presentations about the findings from this study will not include your name or any other information that could ideally give you. By submitting answers to this survey, you are agreeing to participate in this study**

### Cultural Representation

Figure 15: A sample page from the survey presents participants with four images, and below, they are asked to enter a rank between 1 and 4 into designated text fields. Participants provide evaluations based on four criteria: Cultural Representation, Keyword Naturalness, Offensiveness, and Alignment between Description and Image. For each survey item, four images are displayed in a randomized but consistent order throughout the page.





\begin{tabular}{c c l} \hline
**Country** & **Quartile** & **Culture nouns (Count)** \\ \hline \multirow{4}{*}{China} & Q1 & Zhongshan suit (290), Siheyuan (408), Tangzhuang (462) \\ \cline{2-3}  & Q2 & Xiaolongbao (555), Dunhuang (Mogao Caves) (1042), Chinese bamboo steamer (1541), Chinese hot pot (2700), Tulou (2830), Qingming (2901) \\ \cline{2-3}  & Q3 & Yangge (4115), Peking duck (5077), Shanghai (Oriental Pearl Tower) (5522), Chinese wok (5630), Chinese pagoda (10490), Chinese Mid-Autumn Festival (10645), Jade art (10856), Xi'an (Terracotta Army) (12477), Mooncake (15494), Wonton (16147) \\ \cline{2-3}  & Q4 & Beijing (Temple of Heaven) (20108), Lion dance (27302), Qiapo (42352), Beijing (Forbidden City) (55328), Hanfu (107289), Chinese New Year (391554) \\ \hline \multirow{4}{*}{Germany} & Q1 & Rouladenklammer (0), Zwiefacher (6), Deutsch Renaissance (12), Weihnachtsmarkte (17), Nikolaustag (66), Bratpfanne (113), Schuphattler (129), German Romanesque (138), Knodel (409), Schurze (459) \\ \cline{2-3}  & Q2 & German Gothic (2105), German Baroque (2139), Currywurst (2831) \\ \cline{2-3}  & Q3 & Tracht (3855), Heidelberg (Heidelberg Castle) (4885), Munich (Munich's Marienplatz) (5646), Bratburst (7746), Lederhosen (8854), Berlin (Brandenburger Tor) (9286), Sauerkraut (17848) \\ \cline{2-3}  & Q4 & Dirndl (19876), Bavaria (Neuschwanstein Castle) (21512), Cologne (Cologne Cathedral) (29416), Mulled wine (52917), Oktoberfest (111654) \\ \hline \multirow{4}{*}{India} & Q1 & Gupta architecture (148), Allahabad (Allahabad Fort) (203), Indian stupa (298), Manipuri dance (321) \\ \cline{2-3}  & Q2 & Masala dabba (825), Bharnatatyam (1280), Vada pav (1355), Aloo gobi (1797), Mughal architecture (2736), Delhi (Qutub Minar) (3833) \\ \cline{2-3}  & Q3 & Mughal Painting (4618), Saag (5053), Shalwar kameez (8824), Tawa (8949), Jaipur (Amber Fort) (13909), Paratha (14272), Durga Puja (14904) \\ \cline{2-3}  & Q4 & Dhoti (26053), Biryani (30699), Delhi (Red Fort) (43940), Agra (Taj Mahal) (92631), Holi (124249), Sari (129095), Diwali (207137), Lehenga (261160) \\ \hline \multirow{4}{*}{Japan} & Q1 & Sahimi bech\(\ddot{\textmd{o}}\) (0), Noh mai (15), Edo architecture (207) \\ \cline{2-3}  & Q2 & Shinto architecture (505), Takoyaki pan (629), Gion Matsuri (711), Hunten (756), Gyudon (889), Sanja Matsuri (944), Sappor Snow Festival (1603), Okoonomiyaki (2976), Wagashi (3083), Matsumoto (Matsumoto Castle) (3242) \\ \cline{2-3}  & Q3 & Hakama (4957), Gyoza (6092), Haori (6293), Kyoto (Kinkakujj) (6591), Osaka (Osaka Castle) (7576), Yukata (14470), Tempura (15307) \\ \cline{2-3}  & Q4 & Tokyo (Tokyo Tower) (21830), Torii (23570), Kabuki (27924), Shizuoka (Mount Fuji) (36331), Origami (566794) \\ \hline \multirow{4}{*}{Pakistan} & Q1 & Pakistani Indo-Islamic architecture (0), Pakistani Buddhist architecture (0), Jhumair (1), Hyderabad (Pakka Qila) (10), Multan (Shrine of Bahauddin Zakariya) (19), Pakistani Maythal architecture (29), Khattak dance (32), Degchi (50), Golappa (175), Karachi (Mazar-e-Quad) (264)



\begin{table}
\begin{tabular}{c c l} \hline Q2 & Nihari (733), Islamabad (Faisal mosque) (1591), Karahi (2327), Lahore (Badshahi Mosque) (2607), Vaisakhi (3046) \\ \hline Q3 & Gulab jamun (4181), Kleer (4285), Gharara (6853), Shalwar kameez (8824) \\ \cline{2-3}  & Q4 & Eid al-Fitr (17903), Eid al-Adha (18648), Biryani (30699), Sherwani (34183), Truck art (46479), Lehenga (261160) \\ \hline South Korea & Q1 & Cheoyongmu (1), Gamasot (23), Durumagi (28), Aundubujigae (29), Hangari (49), Buchaechum (49), Jeogori (200), Minhwa (310), Seollal (416) \\ \cline{2-3}  & Q2 & Seoul (Jongmyo Shrine) (523), Danguui (594), Korean pagoda (895), Bingsu (945), Gyeongju (Bulguksa) (1002), Tetokbokki (1026), Suwon (Hwaseong Fortress) (1491), Korean temple (1846), Jeonju (Haonk Village) (3018) \\ \cline{2-3}  & Q3 & Chuseok (3853), Korean New Year (4840), Hanok (5797), Seoul (Gyeongbokgang) (7423), Bibimbap (7718) \\ \cline{2-3}  & Q4 & Hanbook (30133), Kimchi (33340) \\ \hline USA & Q1 & \\ \cline{2-3}  & Q2 & Cotton-Eyed Joe (542), Mission Revival (1909) \\ \cline{2-3}  & Q3 & Buffalo check shirt (4342), American Craftsman (5101), clam chowder (9757), Colonial Revival (11429), American folk art (11432), Barbecue ribs (11789), Chicago (Willis Tower) (12907) \\ \cline{2-3}  & Q4 & Los Angeles (Hollywood Sign) (33952), Buffalo wings (34780), Cast iron skillet (41098), Denim overalls (43346), Butter dish (52346), Corn bread (55906), Quilted vest (61705), Bluegrass (96302), Cowboy hat (112205), San Francisco (Golden Gate Bridge) (120285), Apple pie (137320), New York City (Statue of Liberty) (180139), Memorial Day (270410), Independence Day (424057), Washington (The White House) (603026), Thanksgiving (1375806) \\ \hline Vietnam & Q1 & Vietnamese dynasty architecture (4), Vietnamese still house (15), Ao tu than (17), Vietnamese silk painting (21), Mua lan (36), Vietnamese wok (38), Ao ba ba (43), Vietnamese clay pot (68), Hung Kings Temple Festival (151), Vietnamese MidAutumn Festival (305), Goi cuou (385) \\ \cline{2-3}  & Q2 & Vietnamese pagoda (484), Hnoi (One Pillar Pagoda) (554), Banh Xeeo (658), Quang Nam (My Son Sanctuary) (998), Quan ho (1132), Bun Cha (1301), Hue (Imperial City of Hue) (1814), Vietnamese Lunar New Year (3055), Hanoi (Temple of literature) (3628) \\ \hline Q3 & Non la (5798), Banh mi (8550), Ao dai (12302) \\ \hline Q4 & Hanoi (Old Quarter) (26451), Pho (66696) \\ \hline \end{tabular}
\end{table}
Table 16: Culture noun counts by Quartile of each country

* [150] M. C. R. Adams, A. K. S. Ghahramani, and J. L. Friedman. The role of the deep learning process in a multi-task task. In _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pages 2378-2396, 2014.
* [151] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. _arXiv preprint arXiv:1412.6220_, 2014.
* [152] H. Li, Y. Zhang, X. Liu, and Z. Sun. Deep residual learning with zero-mean squared error. In _Advances in Neural Information Processing Systems (NIPS'09)_, pages 1319-1320, 2009.
* [153] W. Li, Y. Zhang, and B. Xu. Learning from multiple images using convolutional neural networks. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pages 1319-1323, 2009.
* [154] T. Lin, Y. Wang, and B. Xu. Learning from multiple images by jointly optimizing the loss function. In _Proceedings of the IEEE conference on computer vision and pattern recognition_, pages 1319-1323, 2014.

[MISSING_PAGE_POST]

 

Figure 17: **Additional Qualitative Sample for German culture. The top two images show the results of C-TRIP for culture nouns categorized under UC nouns, while the bottom two images present the result for culture nouns categorized under RC nouns.**



Figure 18: **Additional Qualitative Sample for Indian culture. The top two images show the results of C-TRIP for culture nouns categorized under UC nouns, while the bottom two images present the result for culture nouns categorized under RC nouns.**



Figure 19: **Additional Qualitative Sample for Japanese culture. The top two images show the results of C-TRIP for culture nouns categorized under UC nouns, while the bottom two images present the result for culture nouns categorized under RC nouns.**



* [25] M. C. R. Adams, A. J. L. Read, and S. D. Swanson. The role of the design for a new class of complex networks. In _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pages 1-6, 2009.
* [26] P. K. Berman, G. E. Hinton, and I. Goodfellow. Imagenet classification with deep neural networks. In _Advances in Neural Information Processing Systems (NIPS'04)_, pages 378-380, 2004.
* [27] T. H. Nguyen, Y. Bengio, and Z. Chen. Learning to align multiple layers by jointly learning from tiny images. In _Advances in Neural Information Processing Systems (NIPS'04)_, pages 135-140, 2004.
* [28] X. Li, W. Liu, and Q. Weinberger. Deep residual learning for image recognition. In _IEEE International Conference on Acoustics and Speech (ICASSP)_, pages 1-10, 2004.
* [29] X. Li, W. Liu, and Q. Weinberger. Deep residual learning for image recognition. In _International conference on computer vision and pattern recognition_, pages 1-10, 2004.
* [30] X. Li, W. Liu, and Q. Weinberger. Deep residual learning for image recognition. In _ECCV_, pages 1-10, 2004.

[MISSING_PAGE_POST]

 

Figure 21: **Additional Qualitative Sample for Korean culture. The top two images show the results of C-TRIP for culture nouns categorized under UC nouns, while the bottom two images present the result for culture nouns categorized under RC nouns.**



* [26] M. C. Davis, J. R. Friedman, and A. G. Hinton. The image quality of the images. In _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pages 1-40, 2005.
* [27] D. P. Kingma and S. Welling. Adam: a method for stochastic optimization. _arXiv preprint arXiv:1312.6980_, 2013.
* [28] L. Krizhevsky, I. Sutskever, and E. Rabinovich. Imagenet classification with deep convolutional neural networks. _CoRR_, abs/1412.04150v1, 2014.

[MISSING_PAGE_POST]

 

* [23] M. C. R. Adams, A. J. L. Read, and S. D. Swanson. The automated method for image segmentation in the wild