Culturally Aware and Adapted NLP : A Taxonomy and a Survey of the State of the Art (2025)

Abstract: The surge of interest in culture in NLP has inspired much recent research, but a shared understanding of “culture” remains unclear, making it difficult to evaluate progress in this emerging area. Drawing on prior research in NLP and related fields, we propose a fine-grained taxonomy of elements in culture that can provide a systematic framework for analyzing and understanding research progress. Using the taxonomy, we survey existing resources and methods for culturally aware and adapted NLP, providing an overview of the state of the art and the research gaps that still need to be filled.

## introduction culture is rapidly becoming an important research topic in natural language processing (nlp), with a significant recent surge in the number of published papers (figure 1 ). current nlp systems, especially large language models (llms), often lack fairness and diversity in cultural awareness, which leads to biased performance that disproportionately favors certain groups, and causes harm to others @xcite @xcite . to build technology that is equitable, inclusive, and accessible, the nlp community must actively take the initiative, enhancing llms' cultural awareness and adaptability. given the keen interest in this area and its importance for the safety and fairness of llms, it is now important to consolidate existing research on culturally aware and adapted nlp to take stock of the progress made so far and to identify research gaps. however, this is challenged by the lack of a common understanding of the concept of ''culture'' in nlp. prior work in nlp such as that by @xcite laid the vital foundations for understanding how language, culture and society interact. @xcite proposed a simple taxonomy derived from the interaction between language and culture that captures broad elements of culture (linguistic form and style, objectives and values, common ground, and aboutness). more recently, @xcite adopted ''proxies of culture'' (semantic or demographic proxies). while neither provides a shared understanding of culture, perhaps unsurprisingly, language is an essential component of culture in nlp. a shared understanding of culture in nlp could benefit from examining definitions developed in anthropology and social sciences. 1 in these fields @xcite @xcite @xcite @xcite , most definitions of culture involve people, groups, and the interactions within and between individuals and groups. @xcite describes culture as ''ideational'' 2 and ''social''. @xcite describes the locus of culture as: (1) ''within human'' (e.g., concepts, beliefs, i.e., ideational), (2) between ''social interaction among human beings'', and (3) outside of human but ''within the patterns of social interaction''. an examination of such work reveals that social interactions are critical components of culture, in addition to ideational elements. notably, this social aspect is underrepresented in previous cross-cultural nlp research figure 1 : papers title and abstract containing ''culture'' or ''cultural'' published in the main and findings of aacl/eacl/naacl/acl/emnlp and tacl within 5 years, with normalized percentages based on the total number of papers at included venues to date. @xcite . 3 hence, we combine the emphasis on language in prior nlp work, while integrating the significance of social and ideational elements that shape culture. this leads us to a working definition of culture used to taxonomize recent work in nlp in this survey: culture encompasses the collective ideas, shared language, and social practices that emerge from and evolve through human social interactions within a society. grounded in this working definition, we introduce a new fine-grained taxonomy of culture by expanding on the basic categories of prior work @xcite to address above-mentioned issues. we then use this taxonomy to organize existing works in culturally aware and adapted nlp, and identify research gaps. our survey of 127 publications in leading *cl venues (see selection method in appendix a) provides an up-to-date view of cultural adaptation resources and models and identifies areas of progress as well as new research opportunities. we hope our taxonomy and analysis enable and inspire further research in this important emerging area. ## the taxonomy in this section, we present our new taxonomy of culture. unlike previous nlp studies @xcite that sought to define cultural elements, this taxonomy (i) is grounded in well-established elements of culture in anthropology and social sciences @xcite @xcite , (ii) consists of more fine-grained elements than in earlier work, and (iii) allows for a wider consideration of how social factors and variations in humans influence culture. figure 2 presents a taxonomy of cultural elements derived from our working definition in §1, organized into three main branches: ideational, linguistic, and social. 4 the ideational branch ( §3.1; @xcite encompasses the non-material aspects of culture that constitute a way of life, such as values or knowledge. the linguistic branch ( §3.2) focuses on cultural variations in language and linguistic forms, bridging the ideational and social elements of culture. the social branch ( §3.3) covers key factors in social interaction and communication, such as relationships or communicative goals. 5 here, we define each element based on existing research and relating to example tasks in the nlp context. we then provide details and examples from the current literature in §3.1, §3.2, and §3.3. ideational elements are based on wellestablished discussions of culture @xcite @xcite : concepts: basic units of meaning that structure and facilitate thought, bridging sensory experience @xcite , e.g., cuisines (such as schnitzel, ratatouille) or holidays (such as diwali, nowruz). related nlp task examples: question answering, dialogue generation. knowledge: information that can be acquired through education or practical experience, e.g., local agricultural knowledge. related nlp task examples: dialogue generation, reasoning. values: beliefs, desirable end states or behaviors ranked by importance that can guide evaluations of things @xcite . unlike norms and morals, values do not inherently involve ethical judgment, e.g., beauty standards, or perception of hate speech. related nlp task examples: content moderation, debiasing. norms and morals: set of rules or principles that govern people's behavior and everyday reasoning @xcite @xcite , e.g., filial obedience attitude. related nlp task examples: reasoning, safety alignment. artifacts: items that are products of human culture, such as art, poetry @xcite , etc. this is ideational in our taxonomy since we do not work on physical buildings or tools in nlp. related nlp task examples: machine translation of long-form literature, emotion arc analysis of movies, memes classification. linguistic elements relate to language variations in the cultural context, based on existing discussions @xcite : dialects: includes variations of languages in a systematic way @xcite @xcite ; such as dialects continuum, regionalects, sociolects, etc.), e.g., african american (vernacular) english (aae/aave). related nlp task examples: machine translation, debiasing. styles, registers, genres: includes elements such as formality, variations of language in situation and communicative forms @xcite , e.g., formality in text, slang, or specific genres like news, folk tales. related nlp task examples: style transfer, creative writing generation. social elements focus on social interactions and communication among humans within the scope of nlp. leveraging the work of @xcite , we identify relevant elements: relationship: connection between two or more individuals or groups, e.g., father-son, colleagues. related nlp task examples: creative writing generation, dialogue generation. context: the ''containers'' of communications @xcite , which can be linguistic such as surrounding sentences or extra-linguistic @xcite , including social settings (e.g., at a wedding), non-verbal cues (e.g., gesture), or historical contexts (e.g., colonization). related nlp task examples: coreference resolution, pragmatic inference. communicative goals: the intention behind language use, e.g., requests, apologies, persuasion. related nlp task examples: intent classification, emotion classification, human-ai collaboration. demographics: the characteristics of people, e.g., economic income, education level, nationality, location, political view, family status, etc. related nlp task examples: content moderation, personalization. ## elements of culture in current nlp (resources) literature in this section, we survey and categorize nlp resources. ## culturally aware resource acquisition resources discussed in §3 are essential for culturally aware nlp. as additional resources are much needed, this section surveys methods for creating new resources (an overview in appendix figure 4 ). resources can be classified based on their acquisition methods-manual, automatic, or semi-automatic-and their source types: (1) newly created (new, from scratch), or (2) culturally adapted from existing resources (ca, e.g., through translation from the original data, followed by culturally appropriate changes). point (1) captures unique cultural phenomena but is often limited by funding or access to native speakers. point (2) provides an alternative, though accurately reflecting cultural phenomena can be challenging. ## creating culturally adapted models most culturally aware nlp research has focused on resource creation and evaluation, with culturally adapted model development still emerging. here, we review current methods for adapting pre-trained (l)lms, covering in-context and in-weight adaptations (an overview can be found in appendix figure 5 ). we found that current cultural adaptation methods in nlp prioritize technical advancements and isolated cultural elements, measuring effectiveness solely by standard task performance. ## further discussions and recommendations as we have seen, significant work remains to be done on both resources and methods for various elements of culture. an area that requires attention is the overall process of researching culturally aware nlp. as mentioned previously, a key practice is community involvement ( §4) to get the process right @xcite @xcite . it is crucial to assess how target communities can benefit most from technologies. for instance, many dialects are primarily oral, and speech-to-speech or speech-to-text translations could be preferable over text-based applications @xcite . furthermore, ethical data collection practices are also critical and technology ownership must be considered, especially when indigenous and marginalized communities are involved. for best practices, we refer the readers to work such as @xcite , smith (2021), or @xcite for further details. another key consideration is integrating insights from fields beyond nlp. cultural adaptation has long been practiced in areas like video games (o'hagan and mangiron, 2013), movies @xcite , online learning @xcite , and clinical psychology @xcite . these existing practices can serve as a foundation for adapting nlp applications to meet the needs of diverse cultural contexts. here, we summarize and recommend best practices based on our prior discussions and the publications surveyed in the sections referenced below: resource acquisition • §4.1, §6: consult with target cultural groups throughout design and implementation, wherever possible. ## conclusions this work proposes a new extensive taxonomy of culture that expands on earlier works in nlp and is grounded in well-established anthropology and social sciences literature. the taxonomy provides a systematic framework for understanding and tracking progress in the emerging area of culturally aware and adapted nlp. however, our taxonomy is not without its limitations. future research could refine the taxonomy in areas like values or communicative goals by adding further subcategories and providing a better understanding of interactions between the elements of culture (e.g., shifting values' impact on social norms over time). we survey existing resources and methods in this area according to the taxonomy classes, identifying areas of strength as well as areas where research remains to be done. our paper summarizes the state of the art and provides ideas for future research in this exciting and important area. dublin, ireland. association for computational linguistics. https://doi.org/10 .18653/v1/2022.acl-long.261