CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting

Authors: huihan  li
liwei  jiang
nouha  dziri
xiang  ren
yejin  choi
Venue: colmweb_org_COLM_2024_Conference
Published: 2024-03-22 01:02:21

Abstract: As the utilization of large language models (LLMs) has proliferated world-wide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures. In this work, we uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations, and extract symbols from these generations that are associated to each culture by the LLM. We discover that culture-conditioned generation consist of linguistic “markers” that distinguish marginalized cultures apart from default cultures. We also discover that LLMs have an uneven degree of diversity in the culture symbols, and that cultures from different geographic regions have different presence in LLMs’ culture-agnostic generation. Our findings promote further research in studying the knowledge and fairness of global culture perception in LLMs.

Main paper: 
 introduction: 
 While the utilization of large language models (LLMs) has proliferated worldwide, LLMs are showned to manifest cultural biases in the following aspects: models prefers culture names (Tang et al., 2023) , cultural entities (Naous et al., 2023) and etiquette (Palta & Rudinger, 2023; Dwivedi et al., 2023) of western-cultures than non-western cultures, and models' opinions on social matters align more with western values than non-western values (Ryan et al., 2024; Tao et al., 2023; Mukherjee et al., 2023; Durmus et al., 2023; AlKhamissi et al., 2024) .In addition to LLM preference and alignment to cultures, it is also crucial to evaluate whether these models manifest adequate knowledge and fair perception for diverse global cultures during generation. While existing works explore extracting or probing culturerelated knowledge stored in LMs (Keleg & Magdy, 2023; Yin et al., 2022; Nguyen et al., 2023b) , our work focus on uncovering LLMs' perception of global culture that is manifested from culture-conditioned prompted generations.Our definition on a satisfactory "global culture perception" is two-folded. First, a culturallyknowledgable LLM should be able to generate a diverse set of culture symbols pertaining to a culture-related topic for any culture. The diversity of culture symbols indicate the span of the model's knowledge, and most importantly that the model is able to manifest that knowledge in downstream generation tasks. Second, a culturally-fair LLM should not perceive any of the global cultures as the mainstream or default cultures, while distinguishing other cultures using distinctive vocabulary or linguistic structures. (Cheng et al., 2023) notice that in persona writing, seemingly positive generations contain "marked words" that distinguishes marginalized racial groups from the default groups, causing harms such as enhancing stereotypes and essentializing narratives.In this paper, we first present a simple LLM culture perception extraction framework that does not involve human labeling or external knowledge bases, and can be applied on any Figure 1 : We construct 🗺 ⽂ A CULTURE-GEN, a dataset of generations on 8 culture-related topics on 110 countries and regions, using gpt-4, llama2-13b, mistral-7b. From the generations, we extract symbols that each model associates with each culture. Using CULTURE-GEN, we the examine the generations with culture-distinguishing markers, and evaluate the diversity of cultural symbols and LM preferences to cultural symbols in culture-agnostic generations.LLM and study any culture. We first use natural language prompts to elicit LLM generations on 8 culture-related topics for 110 countries and regions, using gpt-4, llama2-13b and mistral-7b. From these generation we extract culture symbols, i.e. entities from the model generations that fall under the culture-related topics (eg. "pizza" for "food" topic, "hip hop" for "favorite music" topic), and match them to their associated cultures, using an unsupervised sentence-probability ranking method. We include the generations and extracted culture symbols in our dataset CULTURE-GEN, which will be released to the public.We then demonstrate how the community can use CULTURE-GEN for uncovering LLM's global culture perceptions. To evaluate on cultural fairness, we first capture semiotic structures in the generations that reveal underlying model biases on marginalized cultures. We find that models tend to precede generations with the word "traditional" for cultures in Asian, Eastern European, and African-Islamic countries, as many as 30% for llama2-13b generations and almost 100% for gpt-4 generations. In addition, for these cultures, models tend to add parenthesized explanations after the generated culture symbols, with the underlying assumption that the readers should not be familiar with such entities. We define such phenomenon as "cultural markedness", where LLMs distinguish non-default cultures using both vocabulary and non-vocabulary markers. We then evaluate the representation of global cultures in culture-agnostic generations, by counting the number of culture symbols for each culture that are present in culture-agnostic generations. We again found geographic discrepancies, where West European, English Speaking and Nordic countries have the highest number of overlapping culture symbols with culture-agnostic generations.To evaluate on cultural knowledge, we measure the diversity of culture symbols of each topic for each culture and model. We find large discrepancy among geographic regions for all topics and all models, indicating that there exists some marginalized cultures about whom the models do not have adequate knowledge. In addition, we find that the diversity of culture symbols have moderate-to-strong correlation with the co-occurrence frequency of a culture name and topic-related keywords in training corpora, RedPajama (Computer, 2023) , with Kendall-τ as high as 0.35. Even though RedPajama is not the exact training data of any of the evaluated models, such correlation suggests that training data plays an important role in model's cultural knowledge.Last but not least, we share our insights from our global culture perception evaluations. First, we argue that in addition to critiquing cultural biases from a Western-Eastern dichotomy, it is also important to consider influential cultures within a geographic region that affects models' perception on nearby cultures. Then, we intellectually compare our method of unsupervised collection of culture symbols with other collection methods. Finally, we suggest further studies to be conducted: 1. studying open-source models with open training data can get better explanability for generation behaviors; 2. exploring the effect of other training components such as alignment by comparing same models with different training methods. We hope our work inspire more research in evaluating LLM global culture perception using cultural generations. 
related work: 
 Recent work in probing and evaluating the cultural representation of LLMs ranges across many areas, such as culinary habits (Palta & Rudinger, 2023) , etiquettes (Dwivedi et al., 2023) , commonsense knowledge Nguyen et al. (2023a) , facts Keleg & Magdy (2023) . In addition, some works evaluate stereotypes that target intersectional demographic groups by prompting LLMs (Ma et al., 2023; Cheng et al., 2023) . Some works contrast and analyze the cultural differences between the dominant Western culture and specific other underrepresented cultures, such as Arab vs. Western (Naous et al., 2023) , India and the West (Khandelwal et al., 2023) .Another line of work explores cultural diversity under multilingual settings (Mukherjee et al., 2023) , incorporating geo-diverse multilingual probing (Yin et al., 2022) , investigating multicultural biases using the Word Embedding Association Test (WEAT) across 24 languages (Mukherjee et al., 2023) , and analyzing cross-lingual semantic alignments of cultural symbols through cultural proxies (Adilazuarda et al., 2024) . In particular, low-resource languages have been studied as media of under-represented cultures like African American (Deas et al., 2023) and Indonesian Wibowo et al. ( 2023) languages and cultures. AlKhamissi et al. (2024) . Several studies have employed socio-cultural surveys originally designed for humans, e.g., the World Values Survey (WVS) and Pew Global Attitudes Survey, to evaluate the cultural understanding of LLMs (Ramezani & Xu, 2023; Tao et al., 2023; Durmus et al., 2023) and extrapolate training data for enhancing model culture awareness (Li et al., 2024) .Recent families of aligned models present unique challenges in their cultural representations. In particular, Ryan et al. (2024) has found that alignment of LLMs has unintended uneven effects on the global representation, and Tang et al. (2023) probes socio-demographic preference biases in model latent representation. To improve cultural alignment, AlKhamissi et al. (2024) proposed anthropological prompting. Finally, using large language model generation to create new resources and benchmarks for cultural knowledge has proven to be a promising direction for increasing data resources for boosting models' multicultural proficiency (Fung et al., 2024; Ziems et al., 2023; Huang & Yang, 2023) . 
collection of culture-gen: 
 Countries and regions as a culture. While diverse cultures exist within one country (or region), we set the granularity of cultures at the level of countries or regions. In our work, we include 110 countries and regions that are represented in World Value Survey (Haerpfer & Kizilova, 2012) (Table 4 ). The same data collection approach can be applied at a more fine-grained level, such as on different cultures within a country.Prompting on culture-related topics. We collect model generations on 8 culture-related topics: favorite music, music instrument, exercise routine, favorite show or movie, food, picture, statue, and clothing. For topics of pictures and statues on the front door, we intended to extract culture symbols that do not belong to the other topics but still have cultural significance. For example, representative animals, religious symbols and historical figures of a culture can be extracted in this way. In downstream tasks settings such as story generation, one may ask the model to describe the picture, statue, decoration, etc. where the object depicted in these have cultural significance, but those objects may belong to different categories for different cultures. Therefore, we used "picture" and "statue" as an all-encompassing category, but what we were really interested in extracting are the objects that the model perceives to be depicted in those pictures and statues.The task is to continue generating from natural language prompts shown in Table 1 . We generate 100 samples for each culture on each topic.Generative language models. In this work, we evaluate three state-of-the-art language models: gpt-4, llama2-13b and mistral-7b. Our methodology can be applied to any generative language models. For all models, we set the same hyperparameters: temperature=1.0, top p=0.95, top k=50 and max tokens=30, and period (".") as the stopping criteria2 . For open-source models, we use the huggingface3 weights and implementations and sample all 100 generations at once (setting num return sequences=100). For gpt-4, we use the OpenAI ChatCompletion API and set n=10 and sample 10 times, as sampling 100 generations in the same API call is extremely time consuming.In total, we collect generations about 110 cultures from 3 state-of-the-art LLMs for 8 culturerelated topics in CULTURE-GEN. 
finding culture symbols in culture-gen: 
 Culture Symbols: concepts of a culture. While the sociology term "culture symbols" refer to symbols (i.e. object, word, or action that represents a concept) identified by members of a culture as representative of that culture (Geertz, 1973) , in this work we extend this meaning to all concepts that the language model perceives to be part of the culture. Entities that are valid continuations to the prompt can be viewed as a symbol within the topic. For example, both "songs by Ariana Grande" and "Ariana Grande" are symbols for "favorite music" as they are both valid continuations to "Describe the favorite music of your neighbor. My neighbor likes listening to . . . ".Step 1: Extracting candidate symbols from CULTURE-GEN generations. We extract candidate symbols, i.e. phrases that may contain culture symbols, from CULTURE-GEN generations using gpt-4-turbo-preview. For each generation, we prompt gpt-4-turbo-preview with:" where we construct the "[sentence]" using the prompt in Table 1 concatenated with the candidate symbol. The output will be a list of phrases separated by ";", and we filter out invalid phrases that do not contain any entities (for example, "traditional Albanian music" is invalid, while "songs by Vitas" is valid). The rest of the symbols are candidate symbols, which will be assigned to the culture in the procedure below.Assigning Candidate Symbols to a Culture. For llama2-13b and mistral-7b, we measure the association of a candidate symbol to a culture as the joint probability of Culture c and Symbol e conditioned on Topic T, written as P(c, e|T)4 . More specifically, we construct a sentence following Table 5 , and calculate the sentence probability using the same model that generated the candidate symbol. Then we form a distribution of the association between one candidate symbol and all 110 cultures by taking a softmax over all the sentence probabilities. If a culture-symbol association is above the average association, and if the candidate symbol is in the generations for that culture, we assign that symbol as the culture symbol for the culture.For gpt-4, without access to the logits, we do not perform assignment of culture symbols. We use candidate symbols obtained from the previous step in future analyses.According to this criteria, a culture symbol may be assigned to multiple cultures. Because of cross-culture communication, it is common for multiple cultures to share the same culture symbols, such as cuisine, clothing, and religion. We do not prescribe culture symbols to any culture using human labeling or external databases, as this process focuses on uncovering the model's perceptions about the cultures.Statistics about Culture Symbols. In total, we extracted 13112 candidate culture symbols for gpt-4, 10172 culture symbols for llama2-13b and 15236 culture symbols for mistral-7b(Table 6 ).Ablation on the representativeness of Culture Symbols across demographics -age and gender. We ablate on age using 17 years old and 70 years old, and on gender using male and female pronouns. The prompt and result is included in Appendix B.1. 
llm global culture perception analysis: 
 In this section, we elaborate on our analysis on the cultural fairness and cultural knowledge of SOTA LLMs using CULTURE-GEN. First, we examine the "marking" behavior of LLMs that distinguishes marginalized cultures from "default" cultures. Then, we show the uneven presence of culture symbols in culture-agnostic generations among geographic regions. Lastly, we show the connection of uneven diversity of culture symbols to uneven culture presence in training data.My neighbor is Algerian. For dinner, my neighbor likes to eat ... Markedness. Linguists utilize the concept of "markedness" to highlight the social differences between an unmarked default group and marked groups (Waugh, 1982) . Cheng et al. (2023) studies marked words in LLM generations that distinguish personas of marked (non-white, non-male) groups from default (white, male) groups, and argues that these marked words reflect patterns of othering and exoticizing certain demographics. We discover two types of "markers" in LLM's culture-conditioned generations: 1) using the word "traditional" while mentioning culture name (vocabulary) and 2) adding parentheses that explain the generated symbols (parentheses). Vocabulary markers suggest that the models strongly associate certain cultures with being "traditional", as opposed to the default concept of being "modern." Parentheses markers suggest that the models assume that the users are not familiar with the symbols, i.e. not from the culture to which the symbols belong to.Measurement. For vocabulary marker, we count the number of generations that contains the word "traditional" or the culture name. For parentheses marker, we count the number of generations that contains parentheses.Geographic Discrepancy in Markedness. Figure 2 shows the average number of generations for each geographic region that contain either type of marker for each topic by mistral-7b. "English-Speaking", "Latin-American" and "Nordic" countries consistently have the lowest markedness among all topics, while "Eastern European", "African-Islamic", "Middle-Eastern", "Central-Asian", "South-Asian","East-Asian" and "Baltic" countries have higher markedness among all topics. Figure 3 illustrates the geographic discrepancy on the vocabulary markedness in "clothing". Countries in "African-Islamic", "Central-Asian", "South-Asian", "Southeast Asian" all have nearly 100% marked generations in gpt-4, and higher degree of markedness than the rest of the geographic regions in llama2-13b. Figure 10 shows a similar trend in parentheses markers, where "English-speaking", "Western-European", "East-Asian" countries have the lowest proportion of parentheses markers.To highlight the significance of markedness in culture-conditioned generations, we estimate the default presence of linguistic markers in Appendix C.The "Othering" in "Traditional Cultures" Othering is a process through which one constructs an opposition between self/in-group and the other/out-group, by identifying some characteristics that self/in-group has and the other/out-group lacks, or vice versa. Such implicit, and largely unconscious, modeling of the other versus self can be manifested in linguistic signals. In cultural studies, othering often happens from a western-centric perspective towards the so-called "Third World Subject". For example, "Orientalism" is a pervasive Western tradition-academic and artistic-of prejudiced outsider-interpretations of the Eastern world (such as Asian, Middle Eastern and North African countries)in the service of the Western world (Australasian, Western European, and Northern American countries).Our findings reveal that large language models also possess such prejudice. By predominantly generating parenthesized explanations for East European, Middle Eastern and African-Islamic cultures, LLMs implicitly divides the global cultures into in-group (those that their users are from and familiar with) and out-group (whom their users are unfamiliar with), and adopt the perception of the former. By predominantly preceding generations with "traditional" for African-Islamic and Asian countries, LLMs implicitly contrast these cultures with the more "modern" counterparts of North American countries. Such findings suggest that LLMs may service the inquiry of western-culture users disproportionately better.Measurement. We measure diversity as the number of unique culture symbols that are assigned to a culture. We count all unique culture symbols in one single generation. Table 7 and Table 8 show the distribution of culture symbols for each geographic region. Even though we do not have access to the exact training data of gpt-4, llama2-13b and mistral-7b, we study the open-source re-creation of the llama2-13b training data, RedPajama (Computer, 2023) . We use the methodology implemented in Elazar et al. (2023) to count the number of documents in RedPajama that contains both the culture name, and any of the topic-related keywords (listed in Table 11 ). See Figure 6 for number of documents that each nationality appears in5 .Moderate-to-strong correlation between culture symbol diversity and culture-topic frequency. We use Kendall-τ correlation to measure the correlation between diversity and culture-topic co-occurrence (Schober et al., 2018) . The correlation is moderate-to-strong for most of the topics and culture (Table 3 ). However, llama2-13b and mistral-7b show different correlation trends. Figure 4 shows negative correlation for llama2-13b on "statue", while mistral-7b has positive correlation for exercise routine. One potential reason may due to the calibration performed on mistral-7b. We discuss other factors that impact the correlation in Section 6.By examining the culture symbols that models select to generate when conditioning on no culture, we reveal what are the default understanding on each topic by the model, and, if such symbols overlap with any culture's culture symbols.Culture-agnostic generations. We extract the default symbols for each topic using the same prompts in Table 1 , but the nationality is not revealed in the instruction. For each topic, we also generate 100 samples using the same hyperparameters as described in Section 3.Geographic discrepancy in presence in culture-agnostic generations. Figure 5 shows the overlap rate of gpt-4 and mistral-7b. mistral-7b in general has higher overlap rate than gpt-4, very likely because gpt-4 has more culture-topical knowledge than mistral-7b. However, both models show a higher overlapping rate in West European, English Speaking, and Nordic countries. 
discussion: 
 In this section, we provide our insights on results from this paper, discuss potential improvements to methodology, and suggest future directions on cultural generation analysis.From number of unique culture symbols to percentage of overlapping culture symbols with culture-agnostic generations, we notice a high variance within each geographic region. While most works on culture bias come to the conclusion of a Western versus Eastern dichotomy, the community should not neglect cultures that have a nonetheless regional impact. For example, "qi gong" as an exercise routine is generated and recognized as a culture symbol for "Austrian", "Chinese", "Macanese", "Malaysian", "Singaporean", "Taiwanese" by mistral-7b, where 5 out of the 6 nationalities are in East Asia and Southeast Asia that have closely related cultures; on the other hand, "swimming" is generated and recognized as a culture symbol for 46 nationalities spanning across all geographic regions. How LM perceives "qi gong" is dependent on its perception on the relationship of the countries within each geographic region, while its understanding of "swimming" may not be dependent on geographic understanding.Our choice of continue-generation task for culture perception evaluation. Works on collecting commonsense knowledge-base Petroni et al. (2019) ; West et al. (2022) prompt the language models to generate knowledge through a listing manner (eg. "Please write 20 short sentences about . . . "), and has been applied to generating cultural commonsense knowedge (Nguyen et al., 2023b) . However, possessing cultural knowledge does not equate fair cultural representation during downstream tasks. For example, in tasks such as story generation or persona writing, models may resort to using cultural symbols that do not belong to a culture. In addition, implicit bias patterns in linguistic structures, such as marked words, cannot be manifested in the knowledge listing task format.For works that aim to collect comprehensive cultural data from LLMs and use it in downstream training or tuning language models, LLM-generated data may contain bias or hallucination. For more accurate categorization of culture symbols, works typically resort to online databases. For example, Naous et al. (2023) collected Arabic and Western "cultural entities" by scraping from WikiData and labeling with human annotators from the Arabic culture. However, this approach is limited to the cultures from which high-quality human annotations are available, and thereby also limiting culture-bias research to such cultures. Since our work focuses on analyzing the cultural perceptions of the LLMs and does not intend the cultural symbols to be used in downstream training, the generations are allowed and intended to exhibit bias. Our approach of automatically extracting culture symbol from cultural generations can encompass any culture of interest, making it applicable in low-resource scenarios.data is required for more accurate attribution of model's culture generation behavior. Since we are unable to get the exact training data for gpt-4, llama2-13b or mistral-7b, all analysis conducted on RedPajama remain as a conjecture. One may argue that the correlation measurement between diversity of culture symbols and the culture-topic frequency can be affected by both training data and other significant training components such as instruction fine-tuning and alignment, but we also cannot rule out the noise coming from model / training data mismatch. OLMo (Groeneveld et al., 2024) is the only large language model that has completely open-sourced training set (Soldaini et al., 2024) , and is the most fitting experiment ground for future work to perform culture perception analyses.As state-of-the-art large language models are trained with more components than supervised fine-tuning, as such alignmnent and instruction tuning, it is important to also understand how such factors affect model's demonstrated cultural perceptions. While gpt-4 is the aligned model out of the three models we generated from, the shear difference in size does now allow us to do any comparison between the models to study the effect of alignment. Within the scope of our work, we were unable to elicit any valid response from aligned versions of llama2-13b (llama2-13b-chat) or mistral-7b (mistral-7b-instruct) because of the safeguard measures. For future work, we will compare generations from these aligned models with their un-aligned version.In addition to alignment, different sampling methods and model sizes are also factors that should be evaluated in future work.Potential mitigation of uneven global cultural perception. Our analyses of markedness, diversity of culture symbols, and default culture symbols show that current language models have uneven cultural perception and inadequate cultural knowledge, especially regarding marginalized cultures. Recent work suggests that such biases may have resulted from monotonic alignment procedures (Ryan et al., 2024) . Therefore, we believe that mitigation of cultural biases lie in two steps: 1) Expanding the coverage of pretraining and instruction-tuning data to include global cultures so as to minimize undesirable behaviors in out-of-distribution settings, and 2) Pluralistic alignment (Sorensen et al., 2024) , where models can recognize and generate according to diverse values and perspectives. Sorensen et al. (2024) has suggested multiple potential directions for achieving pluralistic alignment, such as making models that output the whole spectrum of reasonable responses, models that can be faithfully steered to reflect certain properties or perspectives, and models whose distribution over answers matches that of a given target population.In order to resolve conflicts between different cultures in culture-related generations, we believe that we can choose either of the directions above, depending on the intended usage of the model: For general-purpose models, one may prefer models that output the whole spectrum of reasonable responses; for models developed with an audience in mind, such as personalized models, one may prefer models that can be steered for attributions preferred by that group; for data generation models, one may prefer models who can generate an array of answers during sampling. 
conclusion: 
 In our work, we proposed a framework that extracts global cultural perceptions from three SOTA models on 110 countries and regions on 8 culture-related topics through cultureconditioned generations, and demonstrated how to extract culture symbols from these generations using an unsupervised sentence probability ranking method. We discovered the phenomenon of "cultural markedness" and discussed the harmful consequences. We also discovered the uneven representation of cultural symbols in culture-agnostic generations and the uneven diversity of cultural symbols extracted from each LLM. Lastly, we discussed future directions of exploration. Our findings promote further research in studying the knowledge and fairness of global culture perception in LLMs.Figure 6 : Number of documents in which the country is mentioned in RedPajama. The top 5 mentioned countries/regions are "American", "French", "British", "Chinese" and "Indian". The last 5 mentioned countries/regions are "Andorran", "Macedonian", "Trinbagonian", "Macanese" and "Tajik".  
 
 Appendix: For each model (llama2-13b and mistral-7b) and three representative topics (exercise routine, food, favorite music), we selected the culture with the most culture symbols extracted from each geographic region, totalling 12 cultures. From left to right, the geographic regions read: "Eastern-European", "African-Islamic", "Western-European", "Latin-American", "English-Speaking", "Central-Asian", "South-Asian", "Middle-Eastern", "East-Asian", "Nordic", "Baltic","Southeast-Asian". In Table 7 and 8, we report the mean and average of hit rate, percentage of culture symbols extracted from neutral prompts that are generated in each condition, and the mean and average of new rate, percentage of generated symbols that are not a culture symbol previously detected.We find that in both gender and age conditions for both models, models only generate a part of the culture symbols extracted from the neutral generations (as reported by hit rate). This suggests that by increasing the number of conditions, the generated culture symbols become more homogenous. The rest of the generations are either culture symbols that belong to From left to right, the geographic regions read: "Eastern-European", "African-Islamic", "Western-European", "Latin-American", "English-Speaking", "Central-Asian", "South-Asian", "Middle-Eastern", "East-Asian", "Nordic", "Baltic","Southeast-Asian".other cultures, or symbols not previously recognized as culture symbols (as reported by new rate). The new rate shows that for exercise routines and food, there's only a small proportion of generations that have symbols unseen during neutral generations, and a slightly larger portion for music (understandable, as art genres differ more across subpopulations). We also would like to note that some of the newly discovered are not culture symbols, such as "another form of gentle exercise" in exercise routines of 70 year olds, but rather unique expressions, making the potentially unidentified culture symbols even fewer.Empirically, we find mistral-7b bias towards ranking a fixed set of cultures high on probability, which may result from the prior distribution of cultures in its training set. Therefore, we calibrate its probability with the probability of a sentence that does not contain any culture symbols or nationalities, but only reflects the topic of interest, and we take a softmax over the calibrated the distribution. The calibration sentence for each topic is shown in Table 9 . Table 9 : After each prompt, we append "My neighbor is [nationality]". We use the sentence probability to calibrate the sentence probability of mistral-7b on sentences with culture symbols, to even out the prior of culture distribution in a specific topic.Figure 9 shows the average number of generations that contain either type of marker for each topic. favorite music has the highest markedness of all topics, while music instrument and exercise routine have the lowest markedness. gpt-4 has way higher markedness than llama2-13b or mistral-7b.Table 10 shows the number of vocabulary and parentheses markers in culture-agnostic generations for each topic and each model. Compared to markedness of culture-conditioned generations, the default degree of markedness on culture-related topics is very low. https://github.com/huihanlhh/Culture-Gen/In batch generations, we let the model finish and extract the first segment that ends with a period.https://huggingface.co/modelsFor mistral-7b, we perform a calibration process to mitigate the model's prior bias towards a fixed set of cultures. See Appendix B.2.We found that culture-only occurrence is highly correlated (spearman ρ > 0.9) with culturetopic co-occurrence. Here we only plot culture-only occurrence, while we conduct our study using culture-topic occurrence. 


Decision: The paper presents a novel framework to evaluate the cultural aspects of LLMs via prompting. The study focuses on assessing over-representation, diversity, and the concept of a global culture by analyzing generated text based on country-specific prompts. 

This work received unanimously positive reviews. The reviewers agree that this work is highly relevant to the community, and the proposed framework is well-justified. This work also produces a new dataset, which reviewers agree is useful. There are some clarity issues in the writing that can be fixed in the camera-ready version. Overall, I recommend accepting this paper.

Reviews: review: paper_summary: paper_summary: this works proposes a framework to assess the fair perception of cultural aspects on LLMs, with criterias ranging over-representation,  diversity and the idea of a `global culture` .  This is operationalized by a generation phase (based on custom prompts to obtain sentences dealing with a set of specific topics conditioned by country)
followed by a `culture topic` extraction phase.
Then there are requirements specific to to the concept of global culture perception in terms of diversity and fairness 
The combination both the dataset and the analytical framework allows to undercover a given LLM cultural perception. 

Results are in some sense what we may expect, given what we know (or dont know) about the training datasets used for the target LLMs under study : there are indeed cultures that are mariginalized and also knowledge is in some way directly proportional to frequency metrics
strength_weakness: strength_weakness: The analysis is based on enumeration of possible cases/ scenarios. Therefore it could be hard to assess it generalization power (would need to exhaustively go through all possible cases, attribute combinations )
questions: questions: - is there any way to incorporate the audience into the prompting? For example, i wonder of the emergence of markedness, specifically the use of parenthesis changes if i specify more information . In other words, what conditions the LLMs to provide detailed explanation about a certain topic/ term ? What if the prompt is like "I'm (European/Asian) . My neighbor is ..."  Would the result change based on the extra information im providing (LLM may assume i *know* already about a topic)

- i could be missing some part but, is it possible to obtain an estimation for the markedness in the default case of English ? I guess the purpose of parenthesis usage would be more towards clarifying a topic that is complex and is assumed most people would not know. 

- while probably not in the scope of the current submission, but is there anything to add about using LLMs that are not only English-based ? How accentuated could be the results ? Im wondering there could be differences as language usage would be more grounded
paper_summary: paper_summary: In this paper, the authors have developed natural language prompts to elicit language model perceptions of different cultures (through cultural symbols such as clothing, food, etc.). This study discovers that culture-conditioned generation (i.e., priming the prompt with the phrase - My neighbor is <nationality>) generates more text with othering markers (such as traditional) for less-represented cultures compared to default cultures.
strength_weakness: strength_weakness: The major contribution of this study is analyzing language models' perception toward cultures and possible, othering of marginalized cultures. Both of these aspects need more clarity. Please see questions.
questions: questions: 1. Table 1 - The prompts designed for each topic are ambiguous. For instance, playing could also be in context of sports, theater etc. Likewise for practices (practice archery, practice guitar etc.). I tried these prompts on GPT-4 and the generated text do include these domains. How do the authors ensure that extracted cultural symbols belong to specified topics?

 2. The authors have assumed that all cultures have a picture or statue on the front door - please provide source behind this assumption. 

 3. I am also curios to know how representative these cultural symbols are - for instance, favorite show or movie - how is the distribution across age and gender? I tried prompts in Table 5 - I asked model to predict age and gender along with culture. Mostly, it suggests "younger demographic".

 4. Does the word "traditional" always indicate markedness (in sense of otherness)? There is a wikipage on traditional folk music- https://en.wikipedia.org/wiki/Folk_music#Traditional_folk_music
In Appendix C, the authors also noted this markedness is more common in music category. The same could be said for clothing ("traditional dress or clothing").

 5. Pg 6 - "Table 7 and 8 show the distribution of culture symbols for each geographic region" - Table should be Fig. Some geographic regions have many more countries (e.g., Eastern Europe vs Baltic). How does this influence the outcome (#culture symbols, markedness)? Was any kind of normalisation performed?
paper_summary: paper_summary: Culture-Gen dataset: This work collects LLM outputs (generations) for 8 culture related topics for 110 countries using 3 different LLMs. Further, “culture symbols” are extracted from these outputs and matched to the associated cultures using an unsupervised sentence-probability ranking method. 

Global Culture Perception/Cultural Fairness Evaluation: Two-fold evaluations first looks for “cultural markedness” (distinguishing non-default culture using vocabulary markers (e.g. the word traditional) and non-vocabulary markers (e.g. parenthesized explanations). The work further measures the diversity of the cultural knowledge by counting the number of symbols for a language and finding correlations with the RedPajama dataset count (to illustrate existing problems in training datasets). Finally they also look at preferred cultures in culture-agnostic generations.
strength_weakness: strength_weakness: There is a trade-off between defining cultural symbols accurately versus scraping it from LLMs, as discussed in Section 6. But I wonder whether generating data from sources that you find to be “biased” might have inherent issues in the design?
questions: questions: In section 4, 
- I am not sure how you filter out the invalid phrases that do not contain any entities, like how do you decide that “traditional Albanian music” is invalid whereas “songs by Vitas” is valid.
- In the next paragraph, you mean P(c, e|T) right? There's likely a typo.
paper_summary: paper_summary: In the recent past, there have been several papers that have explored the alignment of LLMs with human cultures, this paper takes a slightly different perspective. The paper explores how LLMs perceive global cultures and how does it reflect in their prompt specific generations. 

In particular, authors prompt various LLMs on 8 different culture-related topics and from the generated responses culture symbols (culture specific topics) are extracted. Subsequently, the extracted culture symbols are classified (using unsupervised techniques) into corresponding region specific culture. This results in a new dataset CULTURE-GEN, having generated responses and culture symbols.

Using the above techniques and dataset, authors perform extensive set of experiments to evaluate LLMs and show the bias in LLMs towards dominant and popular cultures.
strength_weakness: strength_weakness: 1. It is not clear how are cultural markers (vocabulary-based and non-vocabulary-based) obtained? I presume these were obtained via manual inspection of responses but this may not result in an exhaustive list of makers; how could one obtain more markers in an automated fashion, e.g., using LLMs?
2. While the paper performs in-depth analysis and experiments and show the bias in LLM’s perspective; the paper is lacking discussion on possible techniques to mitigate the bias; will standard alignment techniques based on RLHF/DPO suffice but in this case how does one resolve conflict between different cultures? Or one needs to develop a different model for each culture?
3. Authors are measuring culture via various topics but there has been recent work ( https://arxiv.org/abs/2403.15412 ) that argues that since culture is highly subjective, it is best to measure it via various proxies. Authors should discuss how do the topics align with various cultural proxies. 

This is not necessarily weakness, but the paper has many typos that can easily be addressed.
questions: questions: Suggestions:
1. There are few typos that authors should fix, for example, second line: showed —> showed; second-last line of second paragraph; Page-4, the joint probability formula has ’n’ instead of ‘c’; Page 5 first line “We” —> “we”
2. In the related work, authors should also discuss more about proxies used for measure culture since it is hard to define culture. For example, authors can check out a recent survey:   https://arxiv.org/abs/2403.15412

Comments: title: Acknowledging the rebuttal
Reponse
comment: Thank you for your responses!
Thank you for your suggestion! Using negation makes a lot of sense. We will add that to our camera ready experiments too.
Thank you for your detailed answer and for putting the time to perform extra experiments. 

-Regarding Q1. I think that is a feasible way to proceed. Nevertheless, i could not be easy to guarantee the generality of the reuslts as most likely you will end up in a combinatorial problem. Another interesting example could be to use a negation (I am NOT Azerbaijani ... )
Thank you for your response.
Thank you so much for recognizing the contribution of our work! We really appreciated your detailed and informative feedback.
Thank you for your detailed response. I have updated the score based on authors' rebuttal.