Investigating Cultural Alignment of Large Language Models (2024)

Abstract: The intricate relationship between language and culture has long been a subject of exploration within the realm of linguistic anthropology. Large Language Models (LLMs), promoted as repositories of collective human knowledge, raise a pivotal question: do these models genuinely encapsulate the diverse knowledge adopted by different cultures? Our study reveals that these models demonstrate greater cultural alignment along two dimensions—firstly, when prompted with the dominant language of a specific culture, and secondly, when pretrained with a refined mixture of languages employed by that culture. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. Specifically, we replicate a survey conducted in various regions of Egypt and the United States through prompting LLMs with different pretraining data mixtures in both Arabic and English with the personas of the real respondents and the survey questions. Further analysis reveals that misalignment becomes more pronounced for underrepresented personas and for culturally sensitive topics, such as those probing social values. Finally, we introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment. Our study emphasizes the necessity for a more balanced multilingual pretraining dataset to better represent the diversity of human experience and the plurality of different cultures with many implications on the topic of cross-lingual transfer.

## introduction large language models (llms) such as chatgpt have garnered widespread utilization globally, engaging millions of users. users interacting with these models across multiple languages have observed a noteworthy phenomenon: prompting with different languages may elicit different responses to similar queries @xcite . from our observations, one reason for the difference between the responses is that they tend to reflect the culturally specific views commonly expressed by the people which use the same language as the prompt.here, we hypothesize that the root cause of this phenomenon lies in the training data, which encodes different and at times conflicting "knowledge" across different languages. 2 culture is a complicated term and defining it stands at the core of anthropological inquiry. hundreds of definitions exist in literature which cover different aspects of interest @xcite . in this paper, we consider culture as as a multi-faceted inquiry that demonstrates substan-tial diversity among human communities, encompassing worldviews and belief systems. through this lens, we aim to measure the cultural alignment of large language models (llms) by simulating existing surveys that have been carried out by sociologists in specific populations. we utilize the responses from actual survey participants as our reference or gold standard. then we measure the similarity between the model's answer when prompted with the participant's "persona" and the actual survey answer. the term "persona" in this context refers to an explicit description of a survey participant, encompassing various traits of interest such as social class, education level, and age (see section 4.3 for a detailed description). this is done for various llms trained and prompted under different configurations. we use this similarity as a proxy for the degree of a model's knowledge of a particular culture. this enables us to assess the llms' capacity to capture the diversity not only of a specific country but also among individuals within that country. we focus on a survey conducted in two countries: egypt (eg) and the united states of america (us). it covers a diverse demographic set within each country with questions spanning various themes that include topics of social, cultural, material, governmental, ethical, and economic significance. this work primarily explores the impact of the language used for prompting and the language composition of pretraining data on a model's cultural alignment as defined above. we consider two languages for prompting: english and arabic as they are the primary languages used in the surveys. specifically, we consider four pretrained llms: gpt-3.5 3 also known as chatgpt, and three 13b parameter instruction-tuned models. the multilingual mt0-xxl @xcite is trained on a variety of languages, llama-2-13b-chat @xcite which is trained primarily on english data, and acegpt-13b-chat @xcite , a model finetuned from llama-2-13b-chat focusing on arabic. our contributions include highlighting the significant role of language in the perceived, functional cultural alignment in model responses, which is affected by both (1) the language in the pretraining data and (2) that of the prompt. further analysis shows that (3) models capture the variance of certain demographics more than others, with the gap 3 gpt-3.5 is gpt-3.5-turbo-1106 throughout this work. ## research questions prompting language and cultural alignment: we hypothesize that employing the native language of a specific culture will yield greater cultural alignment compared to using a foreign language. for instance, prompting an llm in arabic may achieve higher alignment to a survey conducted in egypt than prompting it in english. pretraining data composition: we hypothesize that, for a fixed model size, pretraining models with a higher proportion of data from a specific culture will lead to an increased alignment with the results of surveys conducted in that culture. for instance, a 13b arabic monolingual model is expected to exhibit higher alignment than a 13b english model for a survey conducted in egypt. personas and cultural topics: we anticipate that misalignment will increase for personas from digitally underrepresented backgrounds. for instance, alignment in both arabic and english tests are expected to be lower for a working-class persona in aswan (a city in the south of egypt) compared to an upper-middle-class persona in cairo (egypt's capital and its most populous city). further, we hypothesize that misalignment will increase for uncommon cultural topics. finetuning models to induce cross-lingual knowledge transfer: we gauge the effect of cross-lingual transfer for models predominantly pretrained on one language but finetuned on another. to answer this question, we use the llama-2-chat-13b model (trained primarily on an english corpus) @xcite and the acegpt-chat-13b model (a llama-2-chat-13b model further finetuned on a corpus of arabic and english data) @xcite . ## anthropological preliminaries the concept of culture undergoes continual transformation, encompassing various elements that evolve with time as well as geographical and historical context. many definitions of culture are traced back to tylor (1871) wherein culture constitutes an integrated body of knowledge, belief, art, morals, law, custom, and any other capabilities and habits expressed by members of a society. in that sense, any reflection of such aspects of life in written records can be considered a cultural trend expressed by that text. a model which expresses views in some aspect of life which is aligned with a group of people is culturally aligned with them in that scenario. an alternative perspective shows culture as patterns of behavior. these patterns, how they are chosen and valued, and their meaning, manifest in different forms, such as linguistic records. in that sense, culture observes behavior through history, and affects it through population dynamics @xcite . the cultural expression of agential members within a society, including artificial agents such as llms, thus affects and is affected by the behavior and recording of ideas by fellow members. models learn effectively from humans and equally impart their learnings upon other humans, distributing their internalized cultural ideas in the process @xcite . ## discussion in section 5.2, we demonstrate that both the language utilized for pretraining and the language employed for prompting contribute to enhancing cultural alignment, particularly for countries where the language in question is prevalent. this observation aligns intuitively with our assumption that a culture primarily generates content in its native language on the internet. during pretraining, a model encodes that cultural knowledge within its parameters; then during inference, the prompting language activates the subnetwork responsible for that encoded knowledge @xcite . this observation further underscores the limitation of current llms in effectively transferring knowledge across different languages. this is particularly evident in languages with different scripts like arabic and english @xcite . however, despite our use of modern standard arabic (msa) as the primary language for representing egyptian culture, it is crucial to note that egyptians do not employ msa in their daily interactions. hence, we posit that employing the egyptian arabic dialect would likely yield even greater alignment, provided that the model is sufficiently trained on this dialect. moreover, within egypt, there exist dialectal variations, similar to differences between various states and ethnic groups in the us. therefore, when assessing cultural alignment, it is imperative to acknowledge the diverse backgrounds within each country; for there exists no singular, narrow egyptian archetype, for example. this is why our study focuses on measuring personas across multiple demographic dimensions. finally, we would like to highlight that cultural information is often difficult to verify and coalesce into knowledge, seeing as there are numerous approaches to collecting evidence, building perspectives, and constructing theorems within cultural topics. we observe in modern large models that cultural-knowledge transfer tends to occur from a few dominant languages (for example english) and cultures into responses for prompts in other languages about other cultures. see arora et al. (2023) measured the extent to which cross-cultural differences are encoded in multilingual encoder-only models by probing them in a cloze-style manner across multiple languages using questions from the wvs and hofstede survey @xcite . @xcite similarly use the hofstede culture survey to assess the crosscultural alignment between chatgpt and certain societies when prompting it in different languages, showing that chatgpt exhibits a strong alignment with american culture and adapts less effectively to other cultural contexts. @xcite demonstrate that multilingual and arabic monolingual lms exhibit trends from western cultures even when prompted in arabic and contextualized within an arabic cultural setting. @xcite propose a novel prompting method aimed at enhancing cultural diversity in llm responses. @xcite demonstrate that llms should not be relied upon as proxies for gauging human opinions, as they do not accurately reflect response biases observed in humans when using altered wording. our work differs from the previously mentioned studies by conducting an in-depth analysis of various demographic dimensions, such as the impact of cultural alignment on digitally underrepresented personas. we also examine the influence of the question topic, the language composition used in pretraining, and the language used during prompting in different llms. ## conclusion & future work in this work, we introduce a framework aimed at assessing the cultural alignment of llms, which measures their ability to capture the cultural trends observed within specific populations. to investigate this, we simulate a survey conducted in both egypt and the us using four distinct llms, each prompted with personas mirroring those of the original participants across six demographic dimensions. the metrics we use compare responses on the persona-level allowing us to analyze the model's alignment with respect to several attributes such as social class and education level. the llms we chose vary in pretraining language compositions, which enable us to evaluate how these factors influence cultural alignment. furthermore, we prompt each model with the languages native to the countries under study and thereby studying the significance of language on cultural alignment with implications to cross-lingual transfer research. finally, we introduce anthropological prompting, a novel method that utilizes a framework adopted from the toolkit of anthropological methods to guide the model to reason about the persona before answering for improving cultural alignment. in future work, we would like to explore our cultural alignment framework on data from more cultures while expanding to more languages, as well as test whether cultural alignment can be used as a proxy metric for cross-lingual knowledge transfer.