Cultural Commonsense Benchmarks

• Cultural commonsense benchmarks are evaluation tools that assess AI systems’ understanding of culturally-specific norms, values, and practices.
• They employ methodologies like native authorship, multi-stage validation, and cultural facet modeling to capture regional and linguistic nuances.
• Benchmarks reveal significant biases in AI performance across cultures, using metrics such as accuracy, entropy, and plausibility ratings to highlight inclusivity gaps.

Cultural commonsense benchmarks are structured evaluation resources and datasets aimed at assessing the ability of artificial intelligence systems—primarily LLMs and multimodal AI models—to reason about, recognize, and respond appropriately to knowledge, practices, values, and norms that are culturally contingent. Unlike generic commonsense benchmarks that emphasize universal or Western-centric knowledge, cultural commonsense benchmarks explicitly incorporate geographical, linguistic, social, and value-related variations, thereby enabling rigorous measurement of culturally situated reasoning and highlighting gaps in AI inclusivity and representation.

1. Scope, Definitions, and Motivations

Cultural commonsense reasoning extends standard commonsense to knowledge that varies across social, linguistic, or geographic contexts. This encompasses not only behavioral customs, rituals, idioms, and social expectations but also worldviews, ethical values, and everyday scenarios interpreted through distinct cultural lenses. Multiple studies have underscored that the majority of LLMs are trained on predominantly Western-centric corpora, leading to inherent biases in performance and output (Nguyen et al., 2022, Fung et al., 14 Feb 2024, Shen et al., 7 May 2024, Karinshak et al., 9 Nov 2024, Mushtaq et al., 14 May 2025). Moreover, even benchmarks labeled as “commonsense” are frequently criticized for conflating referential, encyclopedic, or expert knowledge with true human-centric, culturally rooted knowledge (Do et al., 6 Nov 2024). A consolidated conceptual framework situates “cultural commonsense” as self-acquired, experience-based, and regarded as mutually plausible within a community—often implicit and heuristic rather than explicit or codified (Do et al., 6 Nov 2024).

Motivations for building such benchmarks are twofold:

• To drive the development and evaluation of AI systems capable of nuanced, context-aware interaction across diverse societies.
• To expose and quantify regional, linguistic, and cultural disparities embedded in pretraining, fine-tuning, and evaluation workflows of standard models (Fung et al., 14 Feb 2024, Schneider et al., 19 Feb 2025).

2. Methodologies for Dataset Construction

The construction of cultural commonsense benchmarks involves sourcing, curating, and validating data that reflects both the breadth and nuance of lived cultural experiences:

• Manual Authorship and Native Validation: Datasets such as ArabCulture (Sadallah et al., 18 Feb 2025), AMAMMERε (Acquaye et al., 21 Oct 2024), and Thai Winograd Schemas (Artkaew, 28 May 2024) are built explicitly by engaging native speakers and cultural experts from target regions, ensuring every item is lexically accurate, contextually meaningful, and culturally faithful. Multi-stage validation—including blind rating, plausibility annotation, and context-driven rewriting—enforces high agreement and minimizes cross-cultural noise.
• Cultural Facet and Domain Modelling: Pipelines such as CANDLE (Nguyen et al., 2022) automatically extract, classify, cluster, and score sentences from massive web or Wikipedia corpora, labeling assertions by dimensions like geography, occupation, and cultural facets (food, clothing, rituals, behaviors). Facet assignment, concept clustering, and summarization via LLMs (e.g., GPT-3, BART-based NLI scoring) are used to scale the process (Nguyen et al., 2022, Fung et al., 14 Feb 2024).
• Participatory and Pluralist Protocols: WorldView-Bench (Mushtaq et al., 14 May 2025) and AMAMMERε (Acquaye et al., 21 Oct 2024) endorse pluralistic, multiplex frameworks, seeking not a “single truth” but a distribution of worldviews. This is operationalized by incorporating Likert-scale plausibility judgments, multi-agent answers representing contrasting cultural viewpoints, and entropy-based scoring of output diversity.
• Multimodal, Dialectal, and Geographic Diversity: GIMMICK (Schneider et al., 19 Feb 2025), CulturalVQA (Nayak et al., 15 Jul 2024), and AraDiCE (Mousi et al., 17 Sep 2024) introduce both image/text and speech/text questions, broadening the cultural and sensory grounding. Dataset partitioning spans dozens (or hundreds) of countries, subregions, and dialects, leveraging both synthetic and annotated media, to capture how models perform on both tangible (food, crafts) and intangible (rituals, values) facets.
• Quality Assurance and Metrics: Cultural commonsense datasets consistently employ quality assurance via expert review, crowd-sourced ratings, or cross-regional validation, with LaTeX-expressed formulas for scoring, confidence intervals, entropy (for diversity), and reliability metrics underpinning dataset reliability (Nguyen et al., 2022, Do et al., 6 Nov 2024, Mushtaq et al., 14 May 2025).

3. Benchmarking and Evaluation Protocols

Evaluation methodologies in the cultural commonsense domain are more varied than in generic tasks, combining multiple quantitative and qualitative approaches:

• Accuracy and Performance Metrics: Accuracy, relaxed matching, BLEU, ROUGE, and related metrics are applied to multiple-choice, open-ended, and generation tasks. However, studies stress that for cultural tasks, accuracy alone can mislead if the ground-truth itself is culturally narrow or ambiguous (Do et al., 6 Nov 2024, Palta et al., 6 Oct 2024).
• Plausibility and Human Agreement: Explicit plausibility ratings on each answer choice provide graded, rather than binary, measures—exposing when the “gold” answer does not align with majority or local intuitions (Palta et al., 6 Oct 2024, Nguyen et al., 15 May 2025).
• Entropy and Diversity Indices: WorldView-Bench (Mushtaq et al., 14 May 2025) and LLM-GLOBE (Karinshak et al., 9 Nov 2024) employ entropy-based measures—e.g., Perspective Distribution Score (PDS), normalized entropy—to capture the breadth of referenced cultures in a given output. A response that evenly references all worldviews maximizes entropy and is considered more culturally inclusive.
• LLMs-as-a-Judge and Automated Jury: For value-laden or opinion tasks, ensemble “LLMs-as-a-Jury” pipelines aggregate judgments from several models (e.g., GPT-4, Qwen, Claude, Ernie), using a linear regression over their individual rubrics, and sometimes calibrating with human ratings (Karinshak et al., 9 Nov 2024). This supports scalable, large-sample scoring for free-form outputs.
• Human Simulation and Population Modeling: Some frameworks simulate a human population by repeatedly sampling model responses and comparing the resulting probability distribution with empirical human distributions, directly measuring alignment with population consensus rather than with expert-assigned answers (Nguyen et al., 15 May 2025).
• Cross-Lingual and Code-Switching Robustness: Several benchmarks (e.g., ThaiCLI (Kim et al., 7 Oct 2024), Thai Winograd (Artkaew, 28 May 2024), AraDiCE (Mousi et al., 17 Sep 2024)) measure whether models maintain cultural reasoning ability across translated or native language questions, revealing sharp performance drops and dialect-associated failures.

4. Key Findings and Systematic Challenges

Papers spanning benchmarks in Arabic, Thai, Hakka, Ghanaian, and global contexts consistently reveal several trends and challenges:

• Systematic Cultural Bias: LLMs and LVLMs show higher performance on cultures overrepresented in pretraining data (typically the US, UK, China), with underrepresented cultures (Iran, Kenya, Ghana, Arab regions, Thai, Hakka) displaying performance gaps ranging from 15–50 percentage points depending on task and model (Shen et al., 7 May 2024, Acquaye et al., 21 Oct 2024, Sadallah et al., 18 Feb 2025, Schneider et al., 19 Feb 2025).
• Surface-Driven Reasoning: Even large, bidirectional models often learn only superficial cultural commonsense, with robustness tests (e.g., dual test cases, sensitivity to prompt paraphrasing) showing confusion and inconsistent predictions (Zhou et al., 2019, Palta et al., 6 Oct 2024).
• Difficulty with Intangible Facets: While models regularly identify tangible artifacts (food, attire, celebrations), performance on intangible, abstract, or ethical values (ritual explanations, cultural taboos, value systems) remains notably poorer (Nayak et al., 15 Jul 2024, Schneider et al., 19 Feb 2025, Karinshak et al., 9 Nov 2024). Multimodal cues (images, videos) sometimes help, but only marginally for non-Western content.
• Generalization Limitations: LLMs fine-tuned on one commonsense benchmark perform poorly on out-of-domain (i.e., different dataset or cultural context) questions, with performance loss often exceeding 20–30% (Kejriwal et al., 2020). This phenomenon is often exacerbated by dataset-specific (choice) biases, overfitting to surface statistical patterns, and inadequate coverage of regional cultural details.
• Evaluation Design Pitfalls: Binary or closed-form scoring routinely fails to capture the graded, context-contingent nature of cultural commonsense. More than 20% of MCQ items in mainstream benchmarks are identified as “plausibly problematic,” where human raters disagree with the assigned gold label (Palta et al., 6 Oct 2024).
• Simulation vs. Human Variability: Collective model outputs only modestly correlate with the spread of human population opinions, with even the best model–human correlation peaking near 0.43 (compared to around 0.6 human–human split–half reliability) (Nguyen et al., 15 May 2025). LLMs often display overconfidence in their “majority” judgments and may not model intra-cultural variation adequately.

5. Advances in Benchmark Design and Methodological Innovations

Research on cultural commonsense reasoning has yielded several notable methodological advances:

Benchmark Name	Core Focus	Notable Innovations
CANDLE	CCSK extraction	Zero-shot NLI facet assignment, large-scale clustering, representative summaries
CultureAtlas	Multicultural assertions	Sub-country, ethnolinguistic dimensions, self-contained sentences
ThaiCLI/Thai-H6	Thai linguistic/cultural	Factoid/instruction paired judgments, LLM-as-a-judge protocol
ArabCulture	Arabic societal practices	Authoring by local experts, regional cues, MCQ + explanation evaluation
CulturalVQA/GIMMICK	Multimodal, global breadth	VQA with worldwide images, LVLM+LLM co-evaluation, regional bias analysis
AMAMMERε	US–Ghana contrast	Participatory design, Likert ratings, all-answers and context-specific config
WorldView-Bench	Global worldview inclusivity	Entropy/PDS-based diversity metrics, agent-based multiplex outputs
LLM-GLOBE	Cultural value systems	GLOBE dimension rubrics, LLMs-as-a-jury, closed- and open-ended outputs
RAG-Hakka	Minority cultural domains	Bloom’s Taxonomy, RAG-enhanced model evaluation

These benchmarks increasingly capture the multidimensionality of cultural commonsense—spanning language, values, multimodal signals, and cross-domain reasoning.

6. Open Problems, Recommendations, and Future Directions

Despite progress, several open problems and research directions remain:

• Cultural Coverage and Depth: Most coverage is still skewed toward either highly resourced languages or simplified cultural facets (holidays, foods). There is significant need for datasets reflecting more complex, often unwritten, social norms, values, and intra-cultural variation, including dialectal nuances (Nguyen et al., 2022, Sadallah et al., 18 Feb 2025, Mousi et al., 17 Sep 2024).
• Native Creation vs. Translation: Reliance on translated benchmarks preserves English-centric logic and may fail to bring out culture-specific commonsense (e.g., Lin et al. (2021), reviewed in (Sakib, 16 Jun 2025)). Native generation and participatory validation, as practiced in AMAMMERε (Acquaye et al., 21 Oct 2024), Thai Winograd (Artkaew, 28 May 2024), and ArabCulture (Sadallah et al., 18 Feb 2025), are more effective but require substantial resource investment.
• Pluralist and Graded Evaluation: Future benchmarks should prioritize capturing population-level heterogeneity—moving beyond hard “correctness” to plausibility distributions (Palta et al., 6 Oct 2024, Nguyen et al., 15 May 2025, Mushtaq et al., 14 May 2025). Incorporating cross-cultural population samples in benchmarking and human calibration is key for generalization.
• Mitigating Bias and Improving Cues: Systematic inclusion of external cultural or geographic prompts, as well as prompt designs that stimulate multiplex output (multi-agent, persona-based), have been shown to reduce polarization and increase inclusivity in model responses (Mushtaq et al., 14 May 2025, Schneider et al., 19 Feb 2025).
• Multimodal, Multilingual, and Minoritized Domains: Expansion to vision-language, speech-language, and less-resourced linguistic environments is urgently needed, as demonstrated by GIMMICK, CulturalVQA, and AraDiCE (Schneider et al., 19 Feb 2025, Nayak et al., 15 Jul 2024, Mousi et al., 17 Sep 2024).
• Evaluation Metrics and Reliability: Incorporating entropy-based, confidence interval, and correlation metrics—along with human-in-the-loop cross-cultural jury protocols—offers more robust and explainable comparative analyses (Do et al., 6 Nov 2024, Karinshak et al., 9 Nov 2024).
• Dynamic, Updatable Evaluation: Cultural norms shift over time; maintaining “living” benchmarks with periodic updates has been argued as essential for evolving societal alignment (Kim et al., 7 Oct 2024).

7. Significance for AI Alignment and Societal Impact

Effective cultural commonsense benchmarks are essential for developing LLMs and multimodal systems that are not only linguistically competent but also contextually respectful and globally inclusive. Empirical evidence demonstrates that unchecked, models will default to majority or Western-centric norms—potentially perpetuating cultural erasure or bias (Mushtaq et al., 14 May 2025). Incorporating these benchmarks into development pipelines (for training, evaluation, and deployment) is thus necessary for ethical AI alignment, trustworthiness in multicultural service domains, and supporting knowledge preservation for minority and underrepresented groups (Do et al., 6 Nov 2024, Chang et al., 3 Sep 2024, Mousi et al., 17 Sep 2024).

In summary, cultural commonsense benchmarks provide the empirical, methodological, and normative foundation for advancing AI systems that can reason about, adapt to, and reflect the full spectrum of human cultural diversity. Continued expansion in depth, breadth, methodological rigor, and participatory design will be central to achieving robust and genuinely inclusive AI.