Cultural Commonsense Capability in AI

• Cultural commonsense capability is the integration, representation, and exploitation of culturally-specific knowledge that informs AI systems about localized norms and practices.
• It enhances human-computer interaction by enabling personalized responses, preventing cultural miscommunication, and improving language and vision tasks through context-aware reasoning.
• Recent methodologies combine crowdsourcing, web-scale extraction, and LLM-driven prompts to build diverse datasets that address bias and elevate cultural sensitivity in AI models.

Cultural commonsense capability refers to the integration, representation, and exploitation of commonsense knowledge that is conditioned on particular cultural, geographic, or social contexts. Unlike traditional, supposedly “universal” commonsense, cultural commonsense captures those beliefs, behaviors, and intuitions that are widely—but not universally—held, reflecting the everyday practices and factual assumptions particular to specific cultural groups. Recent computational advances have enabled the collection, formalization, and utilization of cultural commonsense to improve the contextuality, personalization, and social alignment of artificial intelligence systems, particularly in applications such as human-computer interaction (HCI), LLMing, vision-and-language tasks, and intercultural dialogue.

1. Foundations of Cultural Commonsense Knowledge

Cultural commonsense knowledge is defined as the set of widely held, culturally-specific beliefs, practices, and social facts governing behavior in diverse contexts. This knowledge includes both tangible facets (e.g., clothing styles, food habits, rituals) and intangible constructs (e.g., greeting customs, taboo topics, value-laden attitudes) (Acharya et al., 2020, Fung et al., 14 Feb 2024). The significance of cultural commonsense is underscored by the observation that “commonsense” varies systematically across demographic, national, regional, religious, and linguistic communities; what appears self-evident to one group may be unintuitive or even transgressive in another (Gürkan et al., 2023, Acquaye et al., 21 Oct 2024, Sadallah et al., 18 Feb 2025). The practical importance lies in supporting systems that can reason about contextual appropriateness, prevent misunderstandings, and respond with cultural sensitivity.

This form of commonsense can be captured as structured assertions, typically in natural language, that encode culturally contingent norms (e.g., “Tipping is not a common practice in Japan,” or “Avocado is red”—the latter flagged in a Brazilian cultural dataset as illustrative of non-universal consensus) (Anacleto et al., 2010). High-quality resources strive for “self-contained” assertions with explicit disambiguation of pronouns, event contexts, and cultural markers (Fung et al., 14 Feb 2024). Recent benchmarks and resources (e.g., CANDLE, CultureAtlas, CARE, AMAMMER, ThaiCLI, ArabCulture, IndoCulture) range from crowd-sourced and expert-curated datasets to large-scale web-extracted corpora, with a growing emphasis on covering underrepresented languages, regional variations, and ethnolinguistic groups (Nguyen et al., 2022, Fung et al., 14 Feb 2024, Guo et al., 7 Apr 2025, Kim et al., 7 Oct 2024, Sadallah et al., 18 Feb 2025, Koto et al., 2 Apr 2024).

2. Collection and Representation Methodologies

Cultural commonsense knowledge acquisition employs a combination of human-centric, web-scale, and LLM-driven methodologies:

• Crowdsourcing & Expert-driven Annotation: Many datasets (e.g., AMAMMER, ArabCulture, IndoCulture) engage native speakers or cultural experts to write and validate culturally relevant questions or assertions. Multistage annotation is common, involving initial generation, rating of answer plausibility, and consensus validation within demographic groups (Acharya et al., 2020, Acquaye et al., 21 Oct 2024, Sadallah et al., 18 Feb 2025, Koto et al., 2 Apr 2024).
• Web-scale Extraction and Filtering: Frameworks like CANDLE use Named Entity Recognition (NER), lexico-syntactic filtering, and zero-shot facet classification (via NLI models) to extract generic, culturally informative assertions from large text corpora. Clustering (e.g., hierarchical agglomerative clustering of SentenceBERT embeddings) and cluster-based summarization facilitate deduplication and highlight salient concepts (Nguyen et al., 2022).
• Directed Prompting and LLMs: LLMs such as GPT-3.5 are prompted with few-shot exemplars to generate high-diversity, high-recall assertions, with subsequent clustering and generative summarization for quality consolidation (e.g., DC² workflow in MANGO) (Nguyen et al., 16 Feb 2024). Directed prompts also support the extraction of fine-grained cultural profile information—such as gender, age, region, religion, occupation—for each assertion (Fung et al., 14 Feb 2024).
• Graph-based and Ontological Approaches: Several efforts formalize cultural commonsense as knowledge graphs (e.g., OMCS-Br ConceptNets, FOLK/TAF ontologies), encoding multi-relational, culture-tagged facts and values (Anacleto et al., 2010, Giorgis et al., 2023).
• Evaluation & Adversarial Filtering: Negative sampling via adversarial or LLM-driven generation ensures datasets probe both correct and incorrect cultural norms (Fung et al., 14 Feb 2024). Evaluation methodologies increasingly emphasize agreement with population-level human judgments, inter-annotator agreement (e.g., quantified by Spearman’s ρ), and explicit LLM-as-a-judge comparative setups (e.g., ThaiCLI) (Kim et al., 7 Oct 2024, Nguyen et al., 15 May 2025).

3. Applications: Human-Computer Interaction and Generative Systems

Cultural commonsense capability has driven advances across several AI domains:

• Human-Computer Interaction (HCI): Early work demonstrated that culturally filtered commonsense networks, such as ConceptNets differentiated by demographic profile, enable personalizable user interfaces and feedback (e.g., WIHT, PACO-T, Cognitor, “What is it?” educational games) (Anacleto et al., 2010). These applications provide context-aware suggestions, prevent cultural miscommunication, and enhance relevance in educational and collaborative tools.
• LLMing and Dialog Systems: LLMs augmented with explicit cultural assertions (e.g., MANGO [DC²] resource) yield more culturally appropriate, specific, and stereotype-avoiding responses in multicultural dialogue and information retrieval tasks (Nguyen et al., 16 Feb 2024, Guo et al., 7 Apr 2025). The injection of structured knowledge from resources such as CANDLE and CARE improves both knowledge-intensive question answering and alignment with local norms (Nguyen et al., 2022, Guo et al., 7 Apr 2025).
• Vision–Language Multimodal Reasoning: Benchmarks such as GD-VCR and CulturalVQA systematically reveal model deficiencies in geo- and culture-diverse settings. Cultural commonsense is essential for high-level visual reasoning—models must interpret attire, rituals, cuisine, and festival-specific iconography, not just objects or basic scenes (Yin et al., 2021, Nayak et al., 15 Jul 2024). Performance disparities (e.g., >20% gap between Western and African/East Asian images) suggest that without explicit cultural knowledge, visual-linguistic AI remains biased toward high-resource, Western contexts.
• Commonsense Benchmarks and Adaptation: Contemporary benchmarks (AMAMMER, ArabCulture, ThaiCLI, IndoCulture, etc.) expose culture-contingent reasoning challenges that cannot be solved by naive translation or training on English-centered datasets (Acquaye et al., 21 Oct 2024, Sadallah et al., 18 Feb 2025, Kim et al., 7 Oct 2024, Koto et al., 2 Apr 2024). The inclusion of location context, explicit regional markers, and cultural profile cues in prompts has been shown to measurably improve LLM accuracy in many cultural tasks (Koto et al., 2 Apr 2024, Sadallah et al., 18 Feb 2025).

4. Empirical Insights, Model Capabilities, and Sociotechnical Bias

Extensive evaluation has uncovered key patterns and challenges:

• Performance Disparity and Cultural Bias: LLMs, both closed-source and open-weight, achieve higher accuracy for dominant cultures and high-resource languages. There is a consistent 10–30% drop in cultural QA for underrepresented countries (e.g., Iran, Kenya, Arab regions, Ghana, non-English Thai tests), reflecting training corpora imbalances (Shen et al., 7 May 2024, Acquaye et al., 21 Oct 2024, Sadallah et al., 18 Feb 2025, Artkaew, 28 May 2024).
• Effect of Query Language and Context: Prompting in English, even for non-English cultures, yields higher accuracy—a function of training data resource distribution. Prompting in native languages can reduce performance by up to 20% (Shen et al., 7 May 2024). Adding precise cultural or location context to prompts increases model performance (e.g., in IndoCulture, up to +7 points with province-specific cues) (Koto et al., 2 Apr 2024).
• Heterogeneous Human Commonsense: Human populations themselves exhibit significant variance in what is regarded as “commonsensical,” as revealed by large-scale judgment studies. Accurate modeling of such heterogeneity—via consensus and awareness scoring metrics m_i^h = √(c_i^h × a_i^h) rather than rigid ground-truth labels—yields benchmarks that more authentically capture collective cultural knowledge (Nguyen et al., 15 May 2025, Gürkan et al., 2023).
• Preference Learning and Alignment: Incorporating human cultural preference data (e.g., native speaker ratings of model outputs) substantially increases model alignment with actual cultural expectations and outperforms generic preference datasets (sometimes by up to 42%) (Guo et al., 7 Apr 2025). Models with stronger baseline cultural competencies benefit more from additional alignment (Guo et al., 7 Apr 2025).
• Limitations in Generalization and Robustness: Models often learn surface-level patterns, with robustness failures exposed by minimal perturbations (dualed test cases). Models may not adjust predictions as expected even when culturally relevant cues are altered (Zhou et al., 2019, Acquaye et al., 21 Oct 2024).

5. Challenges in Dataset Construction, Evaluation, and Mitigation of Bias

Creating robust measures of cultural commonsense is fraught with difficulties:

• Avoiding Western and Anglocentric Bias: Many translated datasets “inherit” English-centric logic, which can propagate cultural and linguistic distortions, failing to capture native idioms, role expectations, or contextual markers (Sakib, 16 Jun 2025, Fung et al., 14 Feb 2024, Sadallah et al., 18 Feb 2025). The move toward natively constructed, locally validated datasets (e.g., ArabCulture, ThaiCLI, IndoCulture) is a partial remedy.
• Granularity and Coverage: Ensuring datasets reflect intra-national, sub-regional, and ethnolinguistic variations, not just national-level averages, is essential. The CultureAtlas resource covers over 1,000 sub-country regions and 2,500+ ethnolinguistic groups, but many cultural phenomena remain underrepresented (Fung et al., 14 Feb 2024).
• Fair and Adaptive Evaluation: Rigid accuracy metrics or exact-match scoring are insufficient in settings rich with legitimate cultural variation (Nguyen et al., 15 May 2025, Kim et al., 7 Oct 2024). LLM-as-a-Judge protocols, paired-answer ratings, and crowd-sourced majority voting have been adopted in recent work to capture social norms and context-dependent appropriateness (Kim et al., 7 Oct 2024, Artkaew, 28 May 2024).
• Stereotyping, Ethics, and Control: Resources and systems must avoid embedding or perpetuating cultural stereotypes, necessitating annotation for stereotype avoidance and routine auditing for bias (Bhatia et al., 2023, Nguyen et al., 16 Feb 2024). There is ongoing work on red-teaming and explicitly controlling the representation and use of cultural knowledge in generative models (Guo et al., 7 Apr 2025, Nguyen et al., 16 Feb 2024).

6. Future Directions and Research Challenges

Key directions and open questions include:

• Data Diversification and Expansion: Ongoing expansion of datasets into new languages, underrepresented cultural domains (e.g., religious traditions), and richer sub-local details is needed for models to generalize robustly (Fung et al., 14 Feb 2024, Sadallah et al., 18 Feb 2025, Guo et al., 7 Apr 2025).
• Dynamic, Context-Aware Systems: Systems must adjust their cultural inference dynamically, tailoring output to user context and supporting multiple valid “cultural truths” (e.g., via nonparametric Bayesian models for consensus discovery such as iDLC-CCT) (Gürkan et al., 2023).
• Hybrid Training and Multimodal Integration: Methods that combine pre-trained embeddings, explicit cultural edge graphs, structured representations, and large-scale textual or visual data show promise for capturing the multi-faceted nature of cultural commonsense (Bhatia et al., 2023, Nguyen et al., 2022, Yin et al., 2021, Nayak et al., 15 Jul 2024).
• Refined Loss Functions and Alignment Techniques: Exploring the use of auxiliary cultural loss terms during training (e.g., $$\mathcal{L} = \mathcal{L}_{\text{task}} + \lambda \mathcal{L}_{\text{cultural}}$$), joint multicultural preference alignment, and role-play prompting for real-time cultural adaptation (Sakib, 16 Jun 2025, Guo et al., 7 Apr 2025).
• Benchmark Design: The field is moving toward composite evaluation protocols—averaging models’ cultural appropriateness across languages, contexts, and subjective human judgments—to drive the development of truly culturally flexible AI (Nguyen et al., 15 May 2025, Kim et al., 7 Oct 2024).

---

Cultural commonsense capability—encompassing the methods, data, and model architectures for acquiring, representing, and reasoning with culture-anchored everyday knowledge—is now recognized as a critical component for building AI systems that can operate effectively and equitably across global, multicultural environments. Progress hinges on large-scale, culturally grounded datasets; advanced model training and alignment; evaluation protocols sensitive to authentic societal heterogeneity; and ongoing attention to ethical, bias, and representation issues in both technology and its deployment.