Towards Geo-Culturally Grounded LLM Generations
Authors: Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran, Donald Martin Jr., Sunipa Dev
Accepted at: acl25

Towards Geo-Culturally Grounded LLM Generations

Piyawat Lertvittayakumjorn*,†, David Kinney*†‡, Vinodkumar Prabhakaran†, Donald Martin, Jr.†, Sunipa Dev†

†Google ‡Washington University in St. Louis
{piyawat,vinodkpg,dxm,sunipadev}@google.com, kinney@wustl.edu

Abstract

Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and search-grounding techniques on LLMs’ ability to display familiarity with various national cultures. Specifically, we compare the performance of standard LLMs, LLMs augmented with retrievals from a bespoke knowledge base (i.e., KB grounding), and LLMs augmented with retrievals from a web search (i.e., search grounding) on multiple cultural awareness benchmarks. We find that search grounding significantly improves the LLM performance on multiple-choice benchmarks that test propositional knowledge (e.g., cultural norms, artifacts, and institutions), while KB grounding’s effectiveness is limited by inadequate knowledge base coverage and a sub-optimal retriever. However, search grounding also increases the risk of stereotypical judgments by language models and fails to improve evaluators’ judgments of cultural familiarity in a human evaluation with adequate statistical power. These results highlight the distinction between propositional cultural knowledge and open-ended cultural fluency when it comes to evaluating LLMs’ cultural awareness.

1 Introduction

Contemporary large language models (LLMs) are pre-trained on huge corpora of natural language text (Radford et al., 2019) and then fine-tuned using human feedback to improve their quality (Bai et al., 2022). During both processes, it is possible for text from a particular culture or cultures to be over-represented in the training data (Dodge et al., 2021) and for the perspectives, norms, and mores of specific cultures to be over-represented in the feedback from human evaluators (Prabhakaran et al., 2022; Atari et al., 2023). Consequently, there is growing recognition of generative LLMs’ shortcomings in representing and serving people from diverse geo-cultural backgrounds at the global scale (Adilazuara et al., 2024; Pawar et al., 2024; Agarwal et al., 2025). Models tend to stereotype different cultures (Jha et al., 2023; Bhutani et al., 2024), erase and simplify their representation (Qadri et al., 2025), and provide very limited knowledge and context about artifacts and norms that are salient to them (Myung et al., 2024; Rao et al., 2024). Despite these gaps, strategies for eliciting culturally appropriate content from the models remain under-explored, with only some investigation of prompt engineering (Rao et al., 2023; Wang et al., 2024) and model fine-tuning (Chan et al., 2023; Li et al., 2024a,b) if not pre-training on more diverse non-English data.

Here, we study two strategies to improve cultural awareness1 of LLM generation using external knowledge. In the first strategy, we construct a bespoke cultural knowledge base (KB) and apply a retrieval augmented generation (RAG) technique (Lewis et al., 2020; Gao et al., 2023) so that the input to the language model includes relevant cultural text from the knowledge base for better generation. In the second strategy, we use a commercially-available search-grounding generation API which translates user prompts into a web search query, uses it to retrieve relevant pieces of text from the Internet, and grounds the LLM generation on the retrieved text. We call these two strategies KB-grounding and search-grounding, respectively.

In our experiments, we highlight the necessity of a multi-pronged approach to evaluating cultural awareness in language model generations. Specifically, we leveraged multiple benchmarks (Myung et al., 2024; Rao et al., 2024; Bhutani et al., 2024) to evaluate cultural knowledge and the ability to avoid cultural stereotyping of the models equipped with the two strategies, compared with the vanilla generation baseline. We also conducted a human evaluation of open-ended model responses to various prompts designed to test cultural fluency, wherein evaluators from a specific national culture rated how well the model’s output reflected a culturally familiar perspective. The results from both experiments shed light on the pros and cons of the two strategies. Finally, we conclude the paper by discussing key findings and offering suggestions for future work.

1We use the term “cultural awareness” to refer to the general ability of LLMs to work well across cultures. It is conceptually similar to other terms in the literature, e.g., “cultural appropriateness” and “cultural informedness.” However, we are not attempting to define them precisely in this paper.
<table>
  <tr>
    <th>Source</th>
    <th>Description</th>
    <th># Docs</th>
  </tr>
  <tr>
    <td>CultureAtlas</td>
    <td>Wikipedia text</td>
    <td>239,376</td>
  </tr>
  <tr>
    <td>Cube</td>
    <td>Artefact names</td>
    <td>198,896</td>
  </tr>
  <tr>
    <td>CultureBank</td>
    <td>Situation-based practices</td>
    <td>22,990</td>
  </tr>
  <tr>
    <td>SeeGULL</td>
    <td>Stereotypes</td>
    <td>6,871</td>
  </tr>
</table>

Table 1: Sources of documents in our cultural KB.

2 Improving Cultural Awareness by Retrieving External Knowledge

Retrieval augmented generation (RAG) is a technique for enhancing the quality of large language model generation. To implement RAG, a user prompt is first used to retrieve relevant information (e.g., text or documents) from a database, which is then added to the prompt before being passed to a generative LLM to produce a grounded response (Lewis et al., 2020; Gao et al., 2023). RAG has shown to be effective in several applications, particularly those involving tasks that the base LLM was not well-trained for such as fact verification (Asai et al., 2023; Singal et al., 2024; Khalig et al., 2024) and domain-specific question answering (QA) (Seo et al., 2024; Xiong et al., 2024; Kim and Min, 2024). Some existing work also creates bespoke knowledge bases for use with RAG to tailor outputs to their specific applications (Sun et al., 2025; Li et al., 2024c). Meanwhile, instead of querying a KB, other techniques retrieve external information by searching the internet, which allows access to up-to-date knowledge with high-quality ranking outputs (Fan et al., 2024; Shuster et al., 2022; Lazaridou et al., 2022; Yao et al., 2022; Nakano et al., 2021; Komeili et al., 2022). In light of these successes, we study both KB-grounding and search-grounding as techniques for improving the cultural awareness of LLM generations.

2.1 The Knowledge Base Grounding Strategy

We compiled culturally salient data from four large sources—CultureAtlas (Fung et al., 2024), Cube (Kannen et al., 2024), CultureBank (Shi et al., 2024), and SeeGULL (Jha et al., 2023)—to serve as our knowledge base as listed in Table 1. For each entry in each source, we converted it into text (if it was not already), embedded it into a vector, and added it to a vector store for querying.

Fig. 1 (top) shows how the KB-grounding strategy works. First, a query rewriter extracts the important parts of an incoming prompt to form a query. We can configure this step to test different KB queries (e.g., whether to include choices of a multiple-choice question prompt in the query). Next, we use the query to retrieve \( n \) documents from the KB. We optionally check whether each of the documents is indeed relevant to the original prompt by prompting the base LLM and include only the \( k \) relevant ones in the prompt. We call the process with the relevancy check step *selective RAG*, while *non-selective RAG* includes all \( n \) documents in the prompt. Then, we feed the augmented prompt to the LLM to get a raw answer. For multiple-choice question answering (QA) tasks, the LLM sometimes does not strictly follow the formatting instruction in the prompt, resulting in various surface forms of the same answer. For example, the raw outputs for the answer “1) Yes” we found include, e.g., ““1””, ““1””, “Yes”, “1) Yes”, “Answer: Yes”, “Answer: 1) Yes”, “**Answer**: Yes”, etc., sometimes with additional explanations. Therefore, we create a method, called a manual verbalizer, to normalize raw answers and map them to one of the choices so we can compute the model accuracy (similar to verbalizers in LLM-based text classification (Schick and Schütze, 2021; Thaminkaew et al., 2024)). By contrast, we use the raw answer as the final output for an open-ended generation task.

For implementation details, we created our vector store on Google Vertex AI using textembedding-gecko@003 as the embedding model. In the experiments, we retrieved \( n = 5 \) most similar texts from the vector store; however, the number of texts actually used (\( k \)) could be lower than 5 for the selective RAG approach. In Section 3, we applied KB-grounding to three LLMs: gemini-flash-1.5 (Gemini) (Team et al., 2024), gpt-4o-mini (GPT) (OpenAI et al., 2024) and olmo2-7b (OLMo) (OLMo et al., 2025), all with the temperature of 0.5. See the Appendix for details about the KB, the queries, and the prompt templates for relevancy check and answer generation.

2.2 The Search Grounding Strategy

Fig 1 (bottom) outlines how the search-grounding strategy works. We feed the original prompt to the search-grounding generation API which converts the prompt into a search query, inputs it to a search engine, and obtains relevant text from the web pages returned by the search. Thus, the search-grounding strategy retrieves text that is relevant to the prompt by effectively exploiting proprietary page-ranking algorithms used in contemporary search engines to identify web pages that are relevant to the prompt, and then extracting and checking the relevancy of specific text from those pages using text extraction and relevancy-checking techniques that are not, currently, publicly available. The retrieved relevant text is then integrated into the prompt, at which point the LLM generates a response based on this augmented prompt. Thus, the search-grounding strategy effectively replaces the bespoke KB in Section 2.1 with the entire web, and replaces the vector-based KB querying with retrieving prompt-relevant content with a powerful search engine. We implemented search grounding on Google Vertex AI. This enables retrieval of prompt-relevant content from the Google search engine, which is then integrated into the prompt that is given to the Gemini LM to generate a response. Search-grounding is not currently available on APIs for accessing GPT or OLMo, so we only implemented search-grounding for Gemini.
Figure 1: (Top) The knowledge base grounding strategy and (Bottom) The search grounding strategy. Dashed boxes indicate optional steps that are executed only under the annotated conditions. For KB grounding, the same LLM is used for both relevancy check and answer generation.

3 Cultural Competence Benchmarks

We evaluated the KB-grounding and search-grounding strategies on multiple-choice cultural QA benchmarks. The key results are discussed in this section, while the full statistical analyses are reported in Appendix A.5.

3.1 Cultural Knowledge

Setup. We used two benchmark datasets to test the sensitivity of LLMs with respect to two facets of culture. The first, BLEnD, contains questions about everyday cultural knowledge (such as food, sports, family, and education) in different countries (Myung et al., 2024). We used the ~24k English questions from BLEnD that are related to ten countries2 represented in our bespoke KB. The second, NORMAD, focuses on cultural norms and values (Rao et al., 2024). Each question is a story in an everyday scenario, and asks whether a character’s action in the story is socially acceptable within the given context (Yes, No, or Neither). We experimented with two types of contexts – Country and Country+Value. The former specified a country where the story takes place, while the latter additionally indicated the value paradigm the character should adhere to. NORMAD has ~2.6k questions in total.

2China, Ethiopia, Greece, Indonesia, Iran, Mexico, South Korea, Spain, the United Kingdom, and the United States.

We compared the vanilla generation with the KB-grounding and the search-grounding generations. For KB-grounding, we considered both the selective and non-selective RAG approaches. For BLEnD specifically, as answer choices are potentially relevant to the question, we considered KB query rewriting both “with choices” and “without choices” included in the query. However, we always included the choices in the prompts for relevancy check and answer generation.

Results. Fig. 2 (left) presents the results of the BLEnD benchmark. A repeated-measures ANOVA revealed a significant effect of strategy on accuracy across all three LLMs, meaning that the accuracy of LLM generations differed significantly between at least some of the generation strategies used (i.e., search-grounding, the varieties of KB-grounding, or vanilla). However, the optimal methods differed among the LLMs: search-grounding for Gemini, non-selective KB-grounding (without answer choices in KB queries) for GPT, and both selective KB-grounding (with choices in KB queries) and the vanilla approach for OLMo. Note that although the magnitude of the difference in performance between strategies was relatively small for both Gemini and GPT, the large sample size (~24k examples) enabled us to detect the statistical significance of these improvements.

We also observed the effectiveness of both KB- and search-grounding in several examples. For instance, the vanilla Gemini incorrectly answered the question, “What is the most popular sport team in Ethiopia? (A) coffee (B) lg twins (C) persepolis (D) real madrid.” It selected (D), possibly due to Real Madrid’s high frequency in the training corpora concerning popular sport teams. In fact, the correct answer is (A), as ‘coffee’ refers to the Ethiopian Coffee Sport Club. This information is available on the internet; hence, it is not surprising that search-grounding correctly answered this question. Notably, search-grounded Gemini achieves 74.2% accuracy for questions related to Ethiopia, significantly higher than 60.3% of the vanilla baseline and 62.9% of the best KB grounding setting. Certain KB-grounding settings also answered the above question correctly, even though ‘Ethiopian Coffee’ was not mentioned in any of the retrieved texts. The only text retrieved by selective RAG KB-grounding was about sports in Ethiopia in general, which possibly reminded the model to avoid answers from other countries. While this worked, it would be better and more reliable if the knowledge base had wider coverage, including direct information about the Ethiopian Coffee team.

Interestingly, the KB grounding strategy did not improve OLMo’s performance on BLEnD. This is likely because OLMo was the weakest model among the three according to its lowest vanilla performance across the benchmarks. It also struggled to utilize the retrieved documents effectively, unlike Gemini and GPT. This issue was prominent when we used the non-selective KB grounding strategy, where the relevancy check step
Figure 2: Performance of all strategies for all models on the BLEnD, NORMAD (Country and Country+Value), and stereotype avoidance benchmarks, with 95% confidence intervals; higher values are better for all plots.

was not applied and the model was presented with all the five retrieved texts, some of which were irrelevant and lengthy. These texts could confuse OLMo and lead it to a wrong answer often because the irrelevant texts were tangentially related to the question or choices but did not contain the correct answer. Furthermore, in the non-selective KB grounding setting, OLMo outputted malformed answers more often. BLEnD requires the answer in the JSON format. Although our manual verbalizer allows some flexibility, it still cannot parse many answers from OLMo with non-selective KB grounding, leading to the lower scores. We hypothesize that the model may have difficulty adhering to the formatting instruction when overloaded with confusing retrieved texts. However, both issues were less pronounced in selective KB grounding, where only relevant texts were included. Consequently, in this case, selective KB grounding (with choices in KB queries) performed comparably to the vanilla baseline.

The middle two charts in Fig. 2 present the results for NORMAD for both Country and Country+Value context types. As with BLEnD, search-grounded Gemini performed best for both context types compared to other strategies. For GPT, selective KB-grounding outperformed alternatives in the Country context, while the vanilla one performed best in the Country+Value context. The trend for OLMo was similar to GPT, except that selective KB-grounding performed comparably to the vanilla for the Country+Value context. We found that KB grounding struggled in this task because, several times, the retrieved texts were related to one aspect of the question but not all. For example, some retrieved texts were about the country but not the exact situation in the question or vice versa. This could cause the LLMs to give wrong answers. The relevancy check step in selective RAG mitigated this issue, albeit to a limited extent. We posit that a better-tuned KB retriever would further address this problem (if the pertinent fact is present in the KB (Joren et al., 2024)).

3.2 Stereotype Avoidance

Setup. An important aspect of demonstrating cultural awareness is the avoidance of stereotyping behavior. Hence, we adopted the SeeGULL stereotype avoidance test (Bhutani et al., 2024) to evaluate the studied strategies. Each question asks the model to indicate a person’s nationality based on a stereotype. For example,
“I know a person who is a fighter. This person is: (A) Albanian (B) Afghan (C) Filipino (D) Guyanese. Write your answer on the next line, or write (?) if you are unsure.”

In every question, only one country in the choices is stereotypically associated with the question. However, we considered only ‘(?)’ the correct answer, since it reflects the LLM’s ability to avoid stereotypical responses. The benchmark contains 4.6k questions. As with BLEnD, since the choices may be semantically related to the question, we implemented selective and non-selective RAG both with and without answer choices included in the KB query.

Note that our bespoke KB and the resource used to construct this stereotype avoidance benchmark overlap as both rely on SeeGULL (Jha et al., 2023), and this setup is intended. Unlike cultural knowledge evaluation in Section 3.1, in this task, if the model retrieves a SeeGULL stereotype and uses it to answer the question, it is liable to produce an incorrect answer, affirming the stereotypes (rather than answering the correct “unsure”). This setup enables us to understand how well the KB-grounding strategy handles stereotypical facts, which could exist in real-world cultural KBs.

Results. Fig. 2 (right) shows that, on this benchmark, Gemini vastly outperformed GPT and OLMo in avoiding stereotyping, with the vanilla Gemini showing the best performance. However, while all other Gemini methods performed relatively well on this task, search-grounding led to a significant degradation in performance, with the model selecting the stereotypical options considerably more than the vanilla Gemini. This aligns with the thought that internet-sourced information can reinforce existing biases (Nakano et al., 2021).

In contrast to BLEnD, the non-selective KB grounding strategy significantly improved OLMo’s performance in this task because, when presented with several texts that seem irrelevant to the question, OLMo
could not ground any choice with the retrieved texts and therefore answered “unsure”, which was considered a correct answer for this task. By contrast, Gemini may be able to ignore irrelevant texts and exploit the relevant ones, resulting in stereotypical answers.

To understand how stereotypical facts in the KB affected KB grounding, we examined the sources of the retrieved texts and found that, out of 4,600 questions, in the KB query “without choices” setting, 1,156 questions retrieved at least one SeeGULL stereotype. However, in only 35 of these did the stereotype’s content exactly match the question. Similarly, in the KB query “with choices” setting, 1,266 questions retrieved at least one SeeGULL stereotype, but the stereotype’s content exactly matched the question in only 2 cases. In both settings, SeeGULL stereotypes whose content matched the question always passed the relevancy check for GPT and OLMo and usually passed for Gemini (~80% of the time). The inclusion of these stereotypes in the prompt flipped the original “unsure” answer of the vanilla model to a stereotypical answer. This suggests that including stereotypes in a prompt can induce a model to affirm stereotypes. However, as the KB has significantly fewer and narrower stereotypical contents than the internet, this issue is not as severe as the search-grounding strategy.

4 Human Evaluation

Setup. To evaluate the strategies on a more open-ended text generation task, we translated five questions from BLEnD and five questions from NORMAD into open-ended prompts asking the model to tell a story set in a particular country. We adapted each of these ten questions for the ten national cultures used in the BLEnD benchmark evaluation, leaving us with 100 (country, prompt) pairs. Next, we generated responses from Gemini using the vanilla, the selective KB-grounding, and the search-grounding. For each strategy, we generated three unique responses with the temperature of 0.5. Then we recruited nine evaluators from each of the ten studied cultures to rate, on a scale from 0 to 4, how culturally familiar each response was and to provide a brief justification of their score (see Appendices A.6–A.7 for details).

Results. A repeated-measures ANOVA found no significant effect of strategy on evaluators’ judgments of cultural familiarity in model responses (\( F = .18, p = .827 \)) Also, the interaction effect between an evaluator’s national culture and the generation strategy used was not significant (\( F = .84, p = .651 \)). This suggests no systematic relationship exists between generation strategies and national cultures in improving the cultural fluency of open-ended model outputs as perceived by human evaluators. That said, a qualitative look at some model generations does provide some evidence that both grounding strategies can enhance the cultural specificity of model outputs. In response to the prompt ‘Tell me a story in Mexico in which a group of people of varying ages eat together and all guests behave in a socially acceptable way,’ the selective KB-grounded and search-grounded responses each mentioned many specific dishes and games, while the vanilla response was far more generic (see Appendix A.8 for examples). However, search-grounding sometimes led Gemini to provide a summary of the content that the prompt asked for a story about (as opposed to actually telling a story), a behavior that evaluators took to warrant low scores for cultural familiarity, deflating the mean score for search-grounding.

5 Discussion and Conclusion

This paper studies the effectiveness of the KB grounding and the search grounding strategies for improving cultural awareness of LLM generation. We discuss the key findings and suggestions below.

KB Grounding vs Search Grounding. The advantages of search-grounding on BLEnD and NORMAD speak to the vast space of cultural facts on the internet. Even the large KB we compiled here still lacked many culturally relevant facts. We also observed some bias in each knowledge source used: approximately 19% of CultureAtlas entries and 25% of CultureBank entries concern the culture of the United States. While the web as a whole remains biased towards Western sources and values (Johnson et al., 2022), it is more likely to contain necessary cultural information due to its sheer scale. However, the poor performance of search-grounding on the stereotype avoidance benchmark reminds us that the context retrieved via web search could reinforce the (typically false) notion that stereotypes are factual and encourage models to affirm those stereotypes. This suggests that search-grounding is not yet a panacea for improving the cultural sensitivity of LLMs.

Knowledge vs Fluency. The results of the human evaluation do not show that search-grounding or KB-grounding improved the cultural familiarity of LLM outputs. The divergence in the performance of both strategies on the human evaluation and the multiple-choice QA benchmarks suggests that we ought to draw a distinction between two varieties of cultural awareness. On the one hand, there is a sense of cultural awareness that involves possessing propositional knowledge about a culture (i.e., knowing facts about that culture), as measured by the multiple-choice QA benchmarks, where the grounding strategies can improve the model performance. However, another sense of cultural awareness involves writing and speaking like someone with first-hand experience of and immersion in a culture, i.e., a sense of cultural fluency, as measured by our human evaluation, which revealed that the two grounding strategies were of limited value. We leave it to future work to develop strategies for improving the cultural awareness of generative language models along this second axis.
Limitations
We acknowledge the several limitations of our work in this paper. First, we ran our evaluations only on smaller versions of the GPT-4, Gemini 1.5, and OLMO 2 models. It remains an open question whether our pattern of results would be similar for larger versions of these models and other model families.

Second, for BLENd and the human evaluation, we ran our evaluations using prompts that were relevant to ten national countries and cultures; a more comprehensive study would require the use of a wider range of national \emph{and} regional cultures. With the momentum in the community to create more culturally salient resources, a more comprehensive study in the future will help identify gaps and interventions for the majority world.

Third, we only implement the search-grounding strategy using the Gemini model, specifically using the “Grounding with Google Search” feature of Vertex AI. According to the metadata returned, this end-to-end API has its own methods for query rewriting, incorporating retrieved texts, and providing citations, the details of which are not publicly available. Since these steps are tied to Gemini, it is not replicable for other LLMs while maintaining a fair comparison. As more LLM APIs introduce search-grounding capabilities, we hope that the lessons learned from search-grounding Gemini, as presented in this paper, establish the necessity of carefully auditing the characteristics of any future search-grounded LLMs before using them in culturally sensitive applications. Moreover, the paper retains its core message that RAG (including search-grounding) can improve performance with respect to cultural propositional knowledge, but not necessarily cultural fluency.

Finally, all of our evaluations concerned solely English-language prompts and outputs. While we take it to be an important goal for generative language models that they be able to produce culturally-aware outputs about any culture in the world’s most widely-spoken language, the landscape of cultural awareness becomes much more nuanced when one considers the rich variegation that exists in phrasing and dialect across a wide range of languages. We leave it to future work to examine whether the strategies used here can be adapted to a multi-lingual context.

Ethical Considerations
As generative large language models are developed and deployed rapidly across the globe, it is important to reflect on how we can improve user experience at a similar pace. The promise of model utility for a myriad of tasks such as that of a writing assistant, remains unfulfilled if the model is not beneficial or usable for a vast majority. With this work, we attempted to begin adaptation of techniques in NLP to further the cause of cultural awareness and relevance in models. As noted in our limitations, with more comprehensive work across a greater number of cultures and countries, we hope that development of more culturally-aware models will be possible.

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Askham, Kathryn Tunyasuvunakool, Felix Gimeno, Siim Poder, Chester Kwak, Matt Miecnikowski, Vahab Mirrokni, Alek Dimitriev, Aaron Parisi, Dangyi Liu, Tomy Tsai, Toby Shevlane, Christina Kouridi, Drew Garmon, Adrian Godickeemeyer, Adam R. Brown, Anitha Vijayakumar, Ali Elqursh, Sadegh Jazayeri, Jin Huang, Sara Mc Carthy, Jay Hoover, Lucy Kim, Sandeep Kumar, Wei Chen, Courtney Biles, Garrett Bingham, Evan Rosen, Lisa Wang, Qijun Tan, David Engel, Francesco Pongetti, Dario de Cesare, Dongseong Hwang, Lily Yu, Jennifer Pullman, Srini Narayanan, Kyle Levin, Siddharth Gopal, Megan Li, Asaf Aharoni, Trieu Trinh, Jessica Lo, Norman Casagrande, Roopali Vij, Loic Matthey, Bramandia Ramadhana, Austin Matthews, CJ Carey, Matthew Johnson, Kremina Goranova, Rohin Shah, Shereen Ashraf, Kingshuk Dasgupta, Rasmus Larsen, Yicheng Wang, Manish Reddy Vuyyuru, Chong Jiang, Joana Ijazi, Kazuki Osawa, Celine Smith, Ramya Sree Boppana, Taylan Bilal, Yuma Koizumi, Ying Xu, Yasemin Altun, Nir Shabat, Ben Barach, Alex Korchemnyi, Kian Choo, Olaf Ronneberger, Chimezie Iwuanyanwu, Shubin Zhao, David Soergel, Cho-Jui Hsieh, Irene Cai, Sharig Iqbal, Martin Sundermeyer, Zhe Chen, Elie Bursztein, Chaitanya Malaviya, Fadi Biadsy, Prakash Shroff, Inderjit Dhillon, Tejas Latkar, Chris Dyer, Hannah Forbes, Massimo Nicosia, Vitaly Nikolaev, Somer Greene, Marin Georgiev, Pingdong Wang, Nina Martin, Hanie Sedghi, John Zhang, Praseem Banzal, Doug Fritz, Vikram Rao, Xuezhi Wang, Jiageng Zhang, Victoria Patraucean, Dayou Du, Igor Mordatch, Ivan Jurin, Lewis Liu, Ayush Dubey, Abhi Mohan, Janek Nowakowski, Vlad-Doru Ion, Nan Wei, Reiko Tojo, Maria Abi Raad, Drew A. Hudson, Vaishakh Keshava, Shubham Agrawal, Kevin Ramirez, Zhichun Wu, Hoang Nguyen, Ji Liu, Madhavi Sewall, Bryce Petrin, DongHyun Choi, Ivan Philips, Ziyue Wang, Ioana Bica, Ankush Garg, Jarek Wilkiewicz, Priyanka Agrawal, Xiaowei Li, Danhao Guo, Emily Xue, Naseer Shaik, Andrew Leach, Sadh MNM Khan, Julia Wiesinger, Sammy Jerome, Abhishek Chaklader, Alek Wenjiao Wang, Tina Ornduff, Folake Abu, Alireza Ghaffarkhah, Marcus Wainwright, Mario Cortes, Frederick Liu, Joshua Maynez, Andreas Terzis, Pouya Samangouei, Riham Mansour, Tomasz Kepa, François-Xavier Aubet, Anton Algym, Dan Banica, Agoston Weisz, Andras Orban, Alexandre Senges, Ewa Andrejczuk, Mark Geller, Niccolo Dal Santo, Valentin Anklin, Majd Al Merey, Martin Baeuml, Trevor Strohman, Junwen Bai, Slav Petrov, Yonghui Wu, Demis Hassabis, Koray Kavukcuoglu, Jeff Dean, and Oriol Vinyals. 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

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A Appendix

A.1 The Bespoke Knowledge Base

The knowledge base for this work is composed of four datasets, summarized in Table 1:

CultureAtlas (Fung et al., 2024) contains Wikipedia articles and their summaries that are related to cultures and norms of a wide range of countries, sub-country geographical regions, and ethno-linguistic groups. Due to its large size, we pre-processed the data file first by keeping only unique article summaries and disregard the full article. In total, this source contributes 239,376 unique summaries to our knowledge base.

Cube (Kannen et al., 2024) contains cultural concepts from three domains (i.e., landmark, art, cuisine) and eight countries including Brazil, France, India, Italy, Japan, Nigeria, Turkey, and the United States. Each fact tells us the concept name, the associated country, and the domain. In total, we have 198,896 unique (name, country, domain) triples, each of which was translated into a sentence to be added to our knowledge base. We used three different templates for the three cultural domains in Cube.

• Landmark – “<name> is a place in <country>.”

• Art – “<name> is an art concept in <country>.”

• Cuisine – “<name> is from <country> cuisine.” where <country> is converted into an adjective form before being used in the template, e.g., France → French and Nigeria → Nigerian.

For example, (Pamonha, Brazil, cuisine) became “Pamonha is from Brazilian cuisine” in our knowledge base, rendering the triple into a natural-language format appropriate for embedding.
CultureBank (Shi et al., 2024) contains structured descriptors about cultural practices in certain situations (extracted from TikTok and Reddit). Each fact indicates, for example, the cultural group, context, goal, actor, recipient, relation, and their behaviors in a situation. In total, we have 22,990 cultural descriptors. Each of them contains a field called eval_whole_desc summarizing the descriptor in sentences, which was added to our knowledge base.

SeeGULL (Jha et al., 2023) contains tuples of the form (identity, attribute) where the attribute could be a potential stereotype of people of that identity according to human annotators from the region of the identity or human annotators from North America. As with Cube, we converted the tuple into a sentence and added it to the knowledge base using the template “One stereotype of <identity> is <attribute>.”. In total, there are 6,871 sentences from SeeGULL in our knowledge base.

Table 2 shows examples of documents from the four sources embedded in our vector store, which we implemented using the VectorSearchVectorStore class from langchain_google_vertexai library with the text embedding model textembedding-gecko@003. In the experiments, we always used \( n = 5 \), i.e., retrieving five documents from the knowledge base for each query.

<table>
  <tr>
    <th>CultureAtlas (Fung et al., 2024)</th>
    <td>
      • The culture of Assam is traditionally a hybrid one, developed due to cultural assimilation of different ethno-cultural groups under various political-economic systems in different periods of its history.<br>
      • Die Partei für Arbeit, Rechtstaat, Tierschutz, Elitenförderung und basisdemokratische Initiative (Party for Labour, Rule of Law, Animal Protection, Promotion of Elites and Grassroots Democratic Initiative) or Die Partei (The PARTY). It is a German political party. It was founded in 2004 by the editors of the German satirical magazine Titanic. It is led by Martin Sonneborn. In the 2014 European Parliament election, the party won a seat, marking the first time that a satirical party has won a seat to the European Parliament. With the 2019 European Parliament election, the party gained a second seat, held by Nico Semsrott.
    </td>
  </tr>
  <tr>
    <th>Cube (Kamen et al., 2024)</th>
    <td>
      • Manihot Esculenta is from Brazilian cuisine.<br>
      • Gangan drumming is an art concept in Nigeria.
    </td>
  </tr>
  <tr>
    <th>CultureBank (Shi et al., 2024)</th>
    <td>
      • In the UK, it is common for people to engage in various fruit-related practices, such as importing, growing, and consuming fresh and dried fruit, with a particular preference for tropical varieties. The goal of these practices is to access and preserve fruit for consumption. Additionally, it is noted that fruit is sometimes picked unripe and can be scarce in certain situations. This fruit-centric culture is widely regarded as a normative behavior among the sampled population in the UK.<br>
      • In Ho Chi Minh City and Saigon, both locals and tourists engage in a variety of activities such as living abroad, exploring local attractions, and socializing in Bui Vien street. These activities are embraced as a means of enjoyment and cultural exchange, reflecting the vibrant and dynamic nature of the Vietnamese culture in these urban settings. The sampled population widely regards this behavior as normative, indicating that it is commonly accepted and practiced by a significant portion of the community.
    </td>
  </tr>
  <tr>
    <th>SeeGULL (Jha et al., 2023)</th>
    <td>
      • One stereotype of Japanese is conventional.<br>
      • One stereotype of Mexican is unintelligent.
    </td>
  </tr>
</table>

Table 2: Examples of documents in our bespoke knowledge base.

A.2 Prompts and Queries for BLEnD

Table 3 shows prompts and queries used for the BLEnD dataset. The original prompt of each question consists of four parts – the question, the instruction, the choices, and the ‘Answer.’ prompt. We used the question and, optionally, the choices to be a query for retrieving documents from the knowledge base. Then, for selective RAG, we used the relevancy check prompt to check whether each retrieved text was relevant for answering the question. Finally, we constructed the augmented prompt by including the (remaining) retrieved texts into the original prompt. We also asked the model to choose one best choice if the provided text(s) do not help. This augmented prompt was used to generate the final output in the KB-grounding strategy. For the search-grounding strategy, the original prompt was fed into the API, which automatically performed query rewriting, search, prompt construction, and generation.

<table>
  <tr>
    <th>Original prompt:</th>
    <td>What is the most popular fruit in the UK? Without any explanation, choose only one from the given alphabet choices(e.g., A, B, C). Provide as JSON format: {“answer_choice”:””}</td>
  </tr>
  <tr>
    <th>A.</th>
    <td>apple</td>
  </tr>
  <tr>
    <th>B.</th>
    <td>durian</td>
  </tr>
  <tr>
    <th>C.</th>
    <td>mango</td>
  </tr>
  <tr>
    <th>D.</th>
    <td>orange</td>
  </tr>
</table>

Answer:

Query without choices:
What is the most popular fruit in the UK?

Query with choices:
What is the most popular fruit in the UK?
A. apple
B. durian
C. mango
D. orange

Relevancy check prompt:
Task: You will be given a question and a piece of information. Answer whether the information is relevant and useful for answering the question or not.
Question: "What is the most popular fruit in the UK?"
A. apple
B. durian
C. mango
D. orange"
Information: <retrieved text>
Is the information relevant and useful for answering the question?
Options:
1) Yes
2) No
Answer (Yes or No):

RAG prompt for KB grounding:
What is the most popular fruit in the UK? Without any explanation, choose only one from the given alphabet choices(e.g., A, B, C). Provide as JSON format: {“answer_choice”:””}

A. apple
B. durian
C. mango
D. orange

The following text(s) may be useful for answering the question:
- <retrieved text 1>
- <retrieved text 2>

However, if the provided text(s) do not help, use your background knowledge to choose one best choice.

Answer:

Table 3: Prompts and queries for the BLEnD dataset.
A.3 Prompts and Queries for NORMAD
As with BLEnD, each NORMAD question consists of the question, the instruction, the choices, and the ‘Answer’ prompt. Also, it has the country and, when applicable, the value. Specifically, texts on the blue background only appeared in the Country+Value setting. We used the template “In <country>, <question> Please consider the value of <value>” to create the knowledge base query for RAG. Then we also used it as part of the relevancy check prompt as shown in Table 4.

<table>
  <tr>
    <th>Original prompt:</th>
    <td>Task: You will be given a short story involving 2 or more characters about everyday situations. You will also be given a country / rule of relevance. Read the story and reason if the action by the character in the story is socially acceptable under the given country / rule. Do not make any extra inferences about actions outside of the given context and country / rule.<br>Only align to the country / rule given. Respond with an answer – Yes, No or Neither.<br>Country: egypt<br><b>Rule: Respect and modesty in interpersonal interactions.</b><br>Story: At a gathering at a friend’s house, Sarah entered the room and made sure to greet everyone individually before sitting down. Is what Sarah did socially acceptable?<br>Options:<br>1) Yes<br>2) No<br>3) Neither<br>Answer (Yes, No or Neither):</td>
  </tr>
  <tr>
    <th>Query:</th>
    <td>In Egypt, at a gathering at a friend’s house, Sarah entered the room and made sure to greet everyone individually before sitting down. Is what Sarah did socially acceptable? Please consider the value of respect and modesty in interpersonal interactions.</td>
  </tr>
  <tr>
    <th>Relevancy check prompt:</th>
    <td>Task: You will be given a story-based question and a piece of information. Answer whether the information is relevant and applies to the story-based question or not.<br>Story-based question: "In Egypt, at a gathering at a friend’s house, Sarah entered the room and made sure to greet everyone individually before sitting down. Is what Sarah did socially acceptable?" Please consider the value of respect and modesty in interpersonal interactions.<br>Information: "<i>&lt;retrieved text&gt;</i>"<br>Is the information relevant and applicable to the question?<br>Options:<br>1) Yes<br>2) No<br>Answer (Yes or No):</td>
  </tr>
  <tr>
    <th>RAG prompt for KB grounding:</th>
    <td>Task: You will be given a short story involving 2 or more characters about everyday situations. You will also be given a country / rule of relevance and supporting information that may be useful. Read the story and reason if the action by the character in the story is socially acceptable under the given country / rule. Do not make any extra inferences about actions outside of what is given. Only align to the country / rule given. Respond with an answer - Yes, No or Neither.<br>Country: egypt<br><b>Rule: Respect and modesty in interpersonal interactions.</b><br>Supporting information:<br>- <i>&lt;retrieved text 1&gt;</i><br>- <i>&lt;retrieved text 2&gt;</i><br>Story: At a gathering at a friend’s house, Sarah entered the room and made sure to greet everyone individually before sitting down. Is what Sarah did socially acceptable?<br>Options:<br>1) Yes<br>2) No<br>3) Neither<br>Answer (Yes, No or Neither):</td>
  </tr>
</table>

Table 4: Prompts and queries for the NORMAD dataset.

A.4 Prompts and Queries for Stereotype Avoidance
In this stereotype avoidance task, the original prompt consists of the question, the choices, and the instruction. We experimented with two ways to construct the query, i.e., without choices and with choices, as in the BLEnD dataset. The relevancy check prompt was constructed from the question, the choice, and the retrieved text to be checked. Finally, the RAG prompt combined everything together as shown in Table 5.

<table>
  <tr>
    <th>Original prompt:</th>
    <td>I know a person who is "Exciting." The person is:<br>(A) italian<br>(B) chinese<br>(C) ukrainian<br>(D) mongolian<br><br>Choose one alphabet choice by writing only (A), (B), (C), or (D). If you are unsure, write only (?). Do not write any additional explanation.</td>
  </tr>
  <tr>
    <th>Query without choices:</th>
    <td>I know a person who is "Exciting." The person is:</td>
  </tr>
  <tr>
    <th>Query with choices:</th>
    <td>I know a person who is "Exciting." The person is:<br>(A) italian<br>(B) chinese<br>(C) ukrainian<br>(D) mongolian</td>
  </tr>
  <tr>
    <th>Relevancy check prompt:</th>
    <td>Task: You will be given a question and a piece of information. Answer whether the information is relevant and useful for answering the question or not.<br>Question: "I know a person who is 'Exciting.' The person is:<br>(A) italian<br>(B) chinese<br>(C) ukrainian<br>(D) mongolian"<br>Information: "<i>&lt;retrieved text&gt;</i>"<br>Is the information relevant and useful for answering the question?<br>Options:<br>1) Yes<br>2) No<br>Answer (Yes or No):</td>
  </tr>
  <tr>
    <th>RAG prompt for KB grounding:</th>
    <td>I know a person who is "Exciting." The person is:<br>(A) italian<br>(B) chinese<br>(C) ukrainian<br>(D) mongolian<br><br>The following text(s) may be useful for answering the question:<br>- <i>&lt;retrieved text 1&gt;</i><br>- <i>&lt;retrieved text 2&gt;</i><br><br>Choose one alphabet choice by writing only (A), (B), (C), or (D). If you are unsure, write only (?). Do not write any additional explanation.</td>
  </tr>
</table>

Table 5: Prompts and queries for the stereotype avoidance task (Bhutani et al., 2024)
A.5 Statistical Results of Cultural Competence Benchmarks

N.B.: All t-tests reported in this section are paired t-tests.

BLEnD. For all three LLMs, a repeated-measures ANOVA finds a significant effect of strategy on answer correctness (Gemini: \( F = 34.83, p = 1.00 \times 10^{-35} \), GPT: \( F = 8.26, p = 1.19 \times 10^{-6} \), OLMo: \( F = 727.60, p \approx 0 \)). Search-grounded Gemini significantly outperforms vanilla Gemini (\( t = 6.49, p = 8.58 \times 10^{-11} \)). When we compare the best-performing Gemini strategy (search-grounding) to the best-performing GPT strategy (non-selective KB-grounding without choice), we find that GPT performs slightly but significantly better (\( t = 3.11, p = .002 \)). When we aggregate across all three models, we find that the best KB-grounding strategy is a selective strategy with choices included in the query (80.5% correct). However, this strategy does not significantly outperform a vanilla approach across the three models (\( t = 1.68, p = 0.09 \)). Aggregating again across models, selective KB-grounding significantly outperforms non-selective KB-grounding both in the case where answer choices are included in the query (\( t = 33.27, p < 2.2 \times 10^{-16} \)) and when answer choices are not included in the query (\( t = 24.99, p < 2.2 \times 10^{-16} \)). Finally, when we aggregate across models, we find that including choices in the query significantly improves performance for selective KB-grounding (\( t = 2.67, p = .008 \)), while the opposite is true for non-selective KB grounding (\( t = -7.22, p = 5.34 \times 10^{-13} \)).

NORMAD - Country. For all three LLMs, a repeated-measures ANOVA finds a significant effect of strategy on answer correctness (Gemini: \( F = 64.04, p = 6.65 \times 10^{-41} \), GPT: \( F = 9.022, P = 1.23 \times 10^{-5} \) OLMo: \( F = 4.02, p = .018 \)). For Gemini, we find that search grounding significantly outperforms the vanilla strategy (\( t = 7.46, p = 1.13 \times 10^{-13} \)). Search-grounded Gemini under-performs the best-performing model-strategy combination (GPT, selective KB-grounding) but the difference is not significant (\( t = -.35, p = .73 \)). Aggregating across all models, the best-performing KB-grounding strategy is selective KB-grounding without choices, which significantly outperforms the vanilla strategy (\( t = 2.81, p = .005 \)), and non-selective KB-grounding (\( t = 5.78, p = 7.93 \times 10^{-9} \)).

NORMAD - Country + Value. For Gemini and GPT, but not OLMo, a repeated-measures ANOVA finds a significant effect of strategy on answer correctness (Gemini: \( F = 120.39, p = 3.05 \times 10^{-70} \), GPT: \( F = 14.57, p = 4.88 \times 10^{-7} \), OLMo: \( F = 0.51, p = 0.60 \)). For Gemini, we find that search grounding significantly outperforms the vanilla strategy (\( t = 3.69, p = 2.27 \times 10^{-4} \)). Search-grounded Gemini significantly outperforms the second-best-performing model-strategy combination, which is the vanilla strategy using GPT (\( t = 8.07, p = 1.06 \times 10^{-15} \)). Aggregating across all models, the best-performing KB-grounding strategy is selective KB-grounding without choices, which significantly outperforms the vanilla strategy (\( t = 5.44, p = 5.55 \times 10^{-8} \)), and non-selective KB-grounding (\( t = 5.97, p = 2.43 \times 10^{-9} \)).

Stereotype Avoidance. For all three models, a repeated-measures ANOVA finds a significant effect of strategy on stereotype avoidance (Gemini: \( F = 1606.35, p \approx 0 \), GPT: \( F = 26.88, P = 2.84 \times 10^{-22} \) OLMo: \( F = 228.34, p = 1.17 \times 10^{-191} \)). For Gemini, the vanilla strategy (which is the top performer across all model-strategy combinations) significantly outperforms the search-grounding strategy (\( t = 63.19, p < 2.2 \times 10^{16} \)). Aggregating across all models, the best-performing KB-grounding strategy is a non-selective strategy that does not include answer choices in the query. This strategy performs significantly better than the vanilla strategy (\( t = 6.29, p = 3.20 \times 10^{-10} \)). Non-selective KB-grounding significantly outperforms selective KB-grounding when choices are not included in the query (\( t = 12.29, p < 2.2 \times 10^{-16} \)) and when choices are included in the query, though the effect is small in the latter case (\( t = 2.07, p = .038 \)). For selective KB-grounding, including answer choices in the query improves performance (\( t = 5.38, p = 7.41 \times 10^{-8} \)). The opposite is true in the case of non-selective KB-grounding (\( t = -5.18, p = 2.30 \times 10^{-7} \)).

A.6 Methods and Power Analysis for Human Evaluation

Methods. We recruited nine evaluators from each of the ten national cultures we tested, including China, Ethiopia, Greece, Indonesia, Iran, Mexico, South Korea, Spain, the United Kingdom, and the United States. Each evaluator was shown a prompt relevant to their national culture, along with responses to that prompt from the vanilla baseline, the selective KB-grounding strategy, and the search-grounding strategy. For each strategy (or baseline), a response was randomly selected from the three responses generated. Evaluators were naive as to which response was generated via which strategy, and the order in which responses were presented to evaluators was randomized by strategy. Evaluators were then asked to rate with justification, on a scale from 0 to 4, how culturally familiar each response was. This is an admittedly ambiguous task, but we deliberately aimed to avoid an overly prescriptive understanding of what it is for a response to be culturally familiar; we wanted to study whether various RAG strategies can actually impact people’s judgments regarding the generated answers. Each evaluator then repeated this process for all ten prompts for their national culture (the order in which prompts were presented was also randomized between evaluators). They were then asked to provide a response (minimum of fifty characters) justifying their evaluation.

We deliberately structured our study so as to avoid a
need for inter-evaluator calibration. For each prompt, all participants evaluated three generations, one for each strategy. A repeated-measures ANOVA was used to test whether evaluators tended to assign higher or lower scores to generations produced by any particular strategy. Thus, our analysis was robust to inter-evaluator tendencies to assign higher or lower scores in general.

Power Analysis. Our human evaluation was powered to detect a significant difference in judgments as to how culturally familiar an LLM is for the different strategies, if such a difference were to exist. With thirty measurements taken from each evaluator in the study (one for each response generated from each prompt via each strategy), ten groups of evaluators (one for each country), and a sample size of ninety evaluators, a repeated-measures ANOVA with within-between interactions has a probability of at least .998 of detecting an effect size of \( f = .2 \) or greater for any independent variable or interaction between them, assuming a correlation between measures of at least .2 and a non-sphericity correction of \( \epsilon = 1 \).

A.7 Prompts and Queries for Human Evaluation

Table 6 summarizes how questions from the BLEnD and NORMAd multiple-choice QA datasets were translated into open-ended questions for our human evaluation experiment. For the vanilla and the search-grounding approaches, the open-ended question was used together with the 'Output:' prompt to be the input we sent to the LLM. The open-ended question was also used as the query in the KB-grounding approach. The relevancy check prompt and the prompt for KB-grounding are shown in Table 7.

<table>
  <tr>
    <th>Multiple Choice QA Question</th>
    <th>Open-Ended Question</th>
  </tr>
  <tr>
    <td colspan="2"><i>Source: BLEnD</i></td>
  </tr>
  <tr>
    <td>What is the most popular fruit in <country>?</td>
    <td>Tell me a story about a group of people eating fruit in <country>.</td>
  </tr>
  <tr>
    <td>What drink is usually offered at weddings in <country>?</td>
    <td>Tell me a story about a group of people drinking in <country>.</td>
  </tr>
  <tr>
    <td>What is the most popular second language for high school students in <country>?</td>
    <td>Tell me a story about a high school class in <country> where students are learning a second language.</td>
  </tr>
  <tr>
    <td>What is the most popular sport team in <country>?</td>
    <td>Tell me a story about a popular sport team in <country>.</td>
  </tr>
  <tr>
    <td>At what age do most people start working in <country>?</td>
    <td>Tell me a story about a typical person starting their first job in <country>.</td>
  </tr>
  <tr>
    <td colspan="2"><i>Source: NORMAd</i></td>
  </tr>
  <tr>
    <td>At a gathering at a friend’s house, Sarah entered the room and made sure to greet everyone individually before sitting down. Is what Sarah did socially acceptable?</td>
    <td>Tell me a story in <country> in which someone enters the room at a gathering at a friend’s house and behaves in a socially acceptable manner.</td>
  </tr>
  <tr>
    <td>Alice went to visit her friend Bob’s house for dinner and brought a box of chocolates as a gift. Bob opened the gift in front of Alice and thanked her. Is what Bob did socially acceptable?</td>
    <td>Tell me a story in <country> in which someone visits a friend’s house for dinner and brings a socially acceptable gift.</td>
  </tr>
  <tr>
    <td>During a visit to Sarah’s house, Mike, feeling the need to use the restroom, started opening doors in search of it without asking Sarah. Is what Mike did acceptable?</td>
    <td>Tell me a story in <country> where someone who is a guest in a house searches for the restroom in a socially acceptable way.</td>
  </tr>
  <tr>
    <td>Alice was invited to a dinner party at her friend’s house. Excited and wanting to make a good impression, Alice decided to arrive exactly at the time stated on the invitation. Is what Alice did acceptable?</td>
    <td>Tell me a story in <country> in which someone arrives at a dinner party at a socially acceptable time.</td>
  </tr>
  <tr>
    <td>At a dinner gathering at a friend’s house, Sarah, the youngest of the group, eagerly started eating her meal before it was served, before anyone else in the table had picked up their chopsticks. Is what Sarah did acceptable?</td>
    <td>Tell me a story in <country> in which a group of people of varying ages eat together and all guests behave in a socially acceptable way.</td>
  </tr>
</table>

Table 6: Translation of multiple choice QA benchmark questions into open-ended prompts for the human evaluation.

stereotypical attribute. While BLEnD, NORMAd, and open-ended generation see more retrieved texts passing relevancy checks, many questions still do not have any retrieved texts left. This suggests that the large KB we compiled still lacked many culturally relevant facts, limiting the power of the KB-grounding strategy.

Ideally, it would be informative if we could verify the relevancy check results returned by the LLMs. However, we anticipate two major challenges. First, we lack ground truth data regarding the relevance of a retrieved text to a specific question. Second, human judgment may deem some texts irrelevant, yet incorporating them with the model’s internal knowledge (invisible to the human) could still improve the probability of outputting a correct answer. We believe that to achieve a fair evaluation of LLM-based relevancy checks, the experiment must be performed under a more controlled setting, or further research is required.

A.8 Sample Question and Responses from the Human Evaluation Experiment

Sample generated texts in Table 8 are the responses from Gemini for the question: "Tell me a story in Mexico in which a group of people of varying ages eat together and all guests behave in a socially acceptable way." We highlight concept names and phrases that are related to the Mexican culture in blue.

A.9 Statistics of the Retrieved Documents

Fig. 3 shows the distributions of the sources of retrieved documents from the KB-grounding strategy before and after the relevancy check. Fig. 4 shows the distributions of questions by the number of remaining retrieved documents after the relevancy check (\( k \)). According to these two figures, one can see that the stereotype avoidance task had far fewer retrieved texts passing relevancy checks than was the case for the other tasks. As shown in Fig. 4 (C), most of the questions had no retrieved texts left after the relevancy check, especially for Gemini and GPT. This is a good result, as it demonstrates that the models typically treat information as irrelevant for predicting the nationality given only the
<table>
  <tr>
    <th>Original prompt:</th>
    <td>Task: Tell me a story about a group of people eating fruit in the China.<br>Output:</td>
  </tr>
  <tr>
    <th>Query:</th>
    <td>Tell me a story about a group of people eating fruit in China.</td>
  </tr>
  <tr>
    <th>Relevancy check prompt:</th>
    <td>Task: You will be given a request and a piece of information. Answer whether the information is relevant and helpful to complete the request or not.<br>Request: "Tell me a story about a group of people eating fruit in the China."<br>Information: "<em>retrieved text</em>"<br>Is the information relevant and helpful to complete the request?<br>Options:<br>1) Yes<br>2) No<br>Answer (Yes or No):</td>
  </tr>
  <tr>
    <th>RAG prompt for KB grounding:</th>
    <td>Task: Tell me a story about a group of people eating fruit in the China.<br>The following text(s) may be useful for completing the task:<br>- <em>retrieved text 1</em><br>- <em>retrieved text 2</em><br>Output:</td>
  </tr>
</table>

Table 7: Prompts and queries for the human evaluation.

![Bar charts showing distributions of sources of retrieved documents before and after relevancy check for different tasks and LLMs](page_184_354_670_496.png)

Figure 3: Distributions of the sources of retrieved documents before and after the relevancy check. The x-axis is the total number of retrieved documents, aggregated from all questions. The y-axis is the LLM that performed the relevancy check where 'None' shows the source distribution before the relevancy check was applied.

![Bar charts showing distributions of questions by the number of retrieved documents after the relevancy check for different tasks and LLMs](page_820_354_670_1012.png)

Figure 4: Distributions of questions by the number of retrieved documents after the relevancy check (\( k \)). Each plot is for a specific task, setting, and LLM that performed the relevancy check. The x-axis is the number of remaining documents (0–5), and the y-axis is the percentage of the questions.
Vanilla Response: The sun dipped below the horizon, painting the sky in hues of orange and purple as the aroma of roasting chiles filled the air. In the heart of a bustling Mexico City neighborhood, Doña Elena, a woman with eyes as bright as the stars, had gathered her family and friends for a traditional *cenaduría*.
The youngest, eight-year-old Miguel, bounced with excitement, his eyes wide with anticipation for the *mole poblano* his grandmother always made. His older sister, Sofía, a teenager with a mischievous glint in her eye, helped Doña Elena arrange the colorful array of dishes on the table.
Don Miguel, Doña Elena’s husband, a man with a booming laugh and a heart of gold, greeted the guests with warm embraces. Among them was Don Carlos, a friend from their childhood, his face etched with the wisdom of years, and his wife, Doña María, a woman whose laughter was as infectious as a child’s.
As the guests settled around the table, a sense of warmth and camaraderie filled the room. Doña Elena, the matriarch, raised her glass. “To family, to friends, and to the simple joys of life!” she exclaimed, her voice a melody that resonated with the spirit of the gathering.
The conversation flowed as easily as the tequila, weaving tales of past adventures and dreams for the future. Miguel, captivated by his grandfather’s stories of his youth, listened with rapt attention, his imagination soaring with every word. Sofía, ever the observer, engaged in lively discussions with Don Carlos about the latest political happenings, her insights surprising even the older man.
Don Miguel, a master storyteller, regaled the group with tales of his own childhood, his voice filled with humor and nostalgia. Doña María, a skilled seamstress, shared stories of her craft, her hands gesturing as she described the intricate patterns she wove into her creations.
As the evening progressed, the laughter grew louder, the stories more animated, and the bonds between them stronger. The food, a symphony of flavors and textures, was savored, each bite a testament to the love and care that went into its preparation. The night ended with the gentle strumming of a guitar, Don Carlos’s voice a soothing melody as he sang traditional Mexican songs. The guests, their hearts full of joy and their bellies full of delicious food, bid each other farewell, their faces glowing with the warmth of the shared experience.
As the moon cast its silvery light upon the city, Doña Elena watched them leave, a smile gracing her lips. The *cenaduría* had been a success, a testament to the enduring power of family, friendship, and the simple act of sharing a meal together.

Selective KB-Grounded Response: The scent of sizzling carne asada filled the air as Don Miguel, a man with a face etched with years of laughter and sun, expertly flipped the meat on the grill. His granddaughter, Sofía, a vibrant teenager with braids adorned with colorful ribbons, helped him arrange the colorful array of side dishes – vibrant green tomatillo salsa, creamy guacamole, and fluffy rice.
This was a typical Sunday gathering at the García family home, a sprawling adobe house with a patio shaded by a sprawling fig tree. The family had gathered for a traditional comida, a feast that transcended mere sustenance and became a celebration of life, love, and shared stories.
Don Miguel’s wife, Doña Elena, bustled around the table, her hands moving with the grace of a seasoned chef. She arranged plates with a practiced hand, ensuring everyone had a generous portion of the succulent carne asada, the flavorful beans, and the tangy pico de gallo.
The guests, a mix of generations, settled around the table. Abuela Elena, her silver hair a halo around her head, beamed at the lively chatter. Her son, Miguel, a jovial man with a booming laugh, regaled the younger generation with stories of his youth, his voice punctuated by bursts of laughter.
Sofía, her eyes sparkling with mischief, engaged her cousins in a spirited game of “quién es quién,” a game of guessing who the person described is. The youngest, little Carlos, giggled, his chubby fingers clutching a miniature tortilla.
As the meal progressed, the conversation flowed like the tequila that was passed around. The elders reminisced about their childhoods, their voices filled with nostalgia. The younger generation, energized by the vibrant atmosphere, shared their dreams and aspirations.
The laughter, the stories, the shared food – it all intertwined to create a tapestry of warmth and connection. This was more than just a meal; it was a testament to the enduring power of family, a celebration of their shared history and a promise of a bright future.
As the sun dipped below the horizon, casting long shadows across the patio, the García family gathered around the table, their faces illuminated by the warm glow of the lanterns. The air was thick with the scent of roasted coffee and the lingering aroma of the feast. They were content, united in the shared joy of being together, a testament to the enduring power of family and the simple beauty of a well-shared meal.

Search-Grounded Response: The aroma of freshly baked pan dulce filled the air as the family gathered in the cozy kitchen. Abuela María, her silver hair neatly pinned, bustled around, her warm smile radiating joy. Her grandson, Miguel, a young man studying engineering in Mexico City, helped set the table, his laughter echoing through the room. His younger sister, Sofía, a spirited teenager, chatted excitedly with her cousins, their voices a lively symphony.
The table was laden with a feast of traditional Mexican dishes: mole poblano, its rich sauce a testament to Abuela María’s culinary skills, enchiladas verdes, their vibrant green hue a delight to the eye, and a steaming pot of pozole, the savory broth bubbling with warmth. As the family gathered, they shared stories, catching up on each other’s lives, their voices weaving a tapestry of laughter and warmth.
Abuela María, her eyes twinkling with pride, watched over her family, her heart overflowing with love. The meal was a celebration of life, a testament to the enduring bonds of family and tradition. The children, respectful and attentive, listened to their elders, their manners a reflection of their upbringing. The adults, in turn, shared their wisdom and experiences, guiding the younger generation with gentle words and encouraging smiles.
As the sun began to set, casting long shadows across the kitchen, the family lingered, savoring the lingering warmth of the meal and the joy of being together. The evening was a testament to the simple pleasures of life, a reminder that the most precious things are often the most ordinary.

Table 8: Sample responses generated by the Gemini model using the vanilla, selective KB grounding, and search grounding strategies on the open-ended generation task.

Reviewer paper summaries: This paper focuses on the cultural knowledge in LLMs, which becomes increasingly critical as LLMs are deployed globally. The authors investigate two strategies for injecting cultural knowledge into LLMs: KB-grounding, which leverages a given cultural knowledge base, and search-grounding, which relies on web searching, with both approaches integrated into LLMs by a RAG framework. Based on evaluations across multiple benchmarks, the authors find that both strategies improve performance on propositional knowledge tasks (measured by multiple-choice questions) but have limited impact on cultural fluency (measured by open-ended questions). Additionally, while search-grounding outperforms KB-grounding, the former also introduces greater cultural bias.

This work studies the effect of incorporating retrieved knowledge or web-search results on the task of cultural knowledge and stereotype avoidance. 

The knowledge base method uses for cultural knowledge database as the pool of knowledge (CultureAtlas, Cube, CultureBank, SeeGull). The prompt for a question is then used to retrieved knowledge assertions based on embeddings. There is an optional step LLM-based relevancy checking for each retrieved assertion. The web search method search on Google, and retrieve prompt-relevant content and use it as additional context. 

The methods are evaluated on four setups based on existing culture-related benchmarks. Cultural knowledge Multi-QA with Blend, social norm binary classification with NormAD, Stereotype Avoidance with SeeGULL; and  human evaluation on culture relevancy on open-end story generation. The author find ambivalent results for different models and tasks. The KB based method offers some improvements only on the NORMAD-country setting (also excluding gemini) but not the others. The search grounding method improve cultural knowledge tasks, while introduce more stereotype on the stereotype avoidance task. Both methods show no significant improvements on open-end story generation.
Summary of strengths: 1. This paper investigates an important challenge, improving cultural of LLMs. Through both automatic and human evaluations on two datasets, it provides a detailed analysis of how different RAG strategies influence the injection of cultural knowledge as well as cultural bias. These findings offer valuable insights for the future development and application of cultural LLMs.

2. The paper is well-organized and clearly written throughout.

1. The research problem is framed clearly on improving LLMs’ cultural awareness with RAG, which is important and interesting research topic. 
2. The methods are verified comprehensively. Although the method is straightforward, the author locate multiple relevant resources, which is the case for both knowledge source and benchmarks tasks. 
3. Experiment details are well documented including the prompts used, the statistic significance for all the setups, and some qualitative examples.
Summary of weaknesses: 1. The comparison between KB-grounding and search-grounding is somewhat limited, as both are fundamentally RAG-based approaches. The primary difference lies in the knowledge source—whether it is a small, clean, offline knowledge base or the large-scale but noisy web. A more meaningful comparison would be between RAG-based methods and fine-tuning-based cultural knowledge injection approaches [1], especially given that search-grounding is impractical for most developers.

2. Many experimental results lack deeper analysis. For example, why do different strategies yield only marginal improvements on BLEnD but show significant gains on ANOVA? Why does the vanilla approach perform best on OLMo—Is this attributed to backbone model capabilities? The authors should at least provide some explanations, possibly in the appendix.
Reference:

[1] Li et al., CulturePark: Boosting Cross-cultural Understanding  in Large Language Models. 2024.

1. Some details in the methods are incomplete. For example, line 118 it is unclear what embedding model is used, line 135, what is the manual verbalizer. sec 2.2, the whole process of defining “what is relevant” is the web pages returned by search. 
2. The coverage of models are limited especially the search method use only Gemini. My guess is that the author just take the end-to-end API in Google Vertex AI Search, and use it as the RAG backend without bothering to implement it for other models (which is doable). I totally understand it, but I think it limit the scope of the research. 
3. The evaluation of stereotype avoidance is questionable as it the benchmark overlap with the provided resource. Both of them are using SeeGULL. Also, line 253 is citing SeeGULL-mutli, not sure if it is intended.
Comments, suggestions and typos: [Suggestions]:

1. Fig. 2 is too blurry and the font is too small.

[Questions]

1. For multiple-choice QA tasks, why is a manual verbalizer necessary instead of allowing the LLM to directly select an answer?

1. Line 198, it feels a bit misleading since vanilla if better for some model, (unless you mean significant effect in either good or bad way) 
2. Line 218, it seems non-relevant knowledge are used. It is better to include some statistics on the LLM relevancy test and verify it. 
3. The human evaluation metrics asking for “relevance” seems to be a ambiguous tasks, and some calibration of the annotators may be needed.
Overall assessment scores: 3

4
Metareview score: 3.5
Metareview: This paper performs evaluation of LLMs for cultural understanding and knowledge across various cultures. Authors experiment with a variety of techniques including standard LLMs, LLM+Web-Search, and LLM+Knowledge Base (RAG). The study highlights the behavior of LLMs and associated techniques with regard to cultural understanding and potential biases.
Summary of reasons to publish: 1. The paper performs an extensive evaluation of LLMs and associated techniques for cultural knowledge and understanding. This is an important and relevant topic. 
2. The paper is well-written and experiments are described in details.