Culturally Grounded Commonsense Dataset

• Culturally grounded commonsense reasoning datasets are curated resources that capture everyday human knowledge and reasoning within specific cultural and geographical contexts.
• They are created through diverse methodologies including crowdsourced expert annotations, context-aware text mining, synthetic LLM generation, and adaptive translation.
• These datasets enhance AI evaluation by revealing performance gaps and bias in models, thereby promoting the development of culturally sensitive, robust systems.

A culturally grounded commonsense reasoning dataset is a curated resource that encodes everyday human knowledge and reasoning situated within specific cultural, geographic, or normative contexts. These datasets are designed to evaluate and develop AI systems that go beyond universal or Anglocentric benchmarks, capturing variation in social practices, traditions, and physical-world problem-solving across diverse communities. They arise from explicit annotation, extraction, or algorithmic generation methodologies and increasingly serve as diagnostic and training tools for culturally aware natural language understanding, vision-language reasoning, and physical interaction.

1. Concept and Motivation

Culturally grounded commonsense reasoning datasets operationalize the notion that human commonsense is not monolithic but shaped by sociocultural, linguistic, and geographic factors. Standard commonsense benchmarks—such as CommonsenseQA (Talmor et al., 2018)—primarily capture global associations and everyday reasoning, largely omitting the local specificity that defines much human understanding. Studies show these omissions yield significant performance gaps when evaluating state-of-the-art LLMs and VLMs on tasks outside standard Western-centric settings (Yin et al., 2021, Koto et al., 2 Apr 2024, Sadallah et al., 18 Feb 2025, Choi et al., 14 Sep 2025, Jeong et al., 22 Sep 2025, Satar et al., 20 Sep 2025). The need for cultural nuance is especially crucial in domains such as etiquette, ritual, culinary practice, celebrations, home organization, physical reasoning, and artifact recognition.

2. Dataset Creation Methodologies

Approaches for building culturally grounded commonsense datasets fall into several paradigms:

• Crowdsourced annotation by local experts: Manual construction using domain experts or long-term residents who author culturally congruent questions, premises, and distractors—see IndoCulture (Koto et al., 2 Apr 2024), ArabCulture (Sadallah et al., 18 Feb 2025), Ko-PIQA (Choi et al., 14 Sep 2025), SCB (Satar et al., 20 Sep 2025). Often, annotators must validate cultural relevance and linguistic correctness across fine-grained topics and regions.
• Web-scale text mining with contextual filtering: Extraction of cultural assertions from large corpora, organized by domains such as geography, religion, and occupation. Facet classification is performed (e.g., food, rituals, clothing) via zero-shot NLI (Nguyen et al., 2022). Advanced clustering and scoring aggregate the output for distinctiveness and plausibility (cf. CANDLE, IDF metric).
• Synthetic generation via LLM prompting and verification: Generation of dataset samples guided by human-provided seed examples, with output filtered for fluency and cultural accuracy using trained models (e.g., XLM-R classifier) and peer-reviewed (Pranida et al., 18 Feb 2025).
• Translation and adaptive rewriting: Existing benchmarks like COPA (Ponti et al., 2020) are translated and culturally adapted by native speakers, who ensure that concepts are not simply literal but recontextualized for local relevance (e.g., substitute “bowling ball” for a familiar local item).
• Scene and object graph-based visual annotation: In multimodal datasets, images displaying cultural artifacts are selected, with bounding polygons or masks for fine-grained artifacts; QA pairs are authored by annotators familiar with cultural norms (Yin et al., 2021, Satar et al., 20 Sep 2025).
• Iterative, active perception pipelines: Datasets such as MessySurfaces (Kwon et al., 2023) use real-world, multi-view images for incremental robotic reasoning, enabling culturally variable interpretations of normative actions.

3. Dataset Composition and Scope

Culturally grounded datasets are characterized by breadth and granularity:

• Language and Geography Coverage: Datasets such as XCOPA (Ponti et al., 2020) and IndoCulture (Koto et al., 2 Apr 2024) span dozens of languages or regional cultures, utilizing typological diversity indices to maximize coverage.
• Domains and Facets: Commonly covered topics include food, holidays, rituals, weddings, family relations, social norms, clothing, idioms, physical problem-solving (e.g., kimchi fermentation in EPiK (Jeong et al., 22 Sep 2025)), and artifact identification (SCB (Satar et al., 20 Sep 2025)).
• Annotation Schema: Datasets typically use multiple-choice formats; some adopt sentence completion, binary choice, or generative inference. EXAMPLES:
  • ArabCulture: 3,482 MCQs across 13 countries, 12 domains, 54 subtopics (Sadallah et al., 18 Feb 2025).
  • IndoCulture: Sentence completion for 11 provinces, 12 topics, 66 subtopics (Koto et al., 2 Apr 2024).
  • Ko-PIQA: Physical commonsense, 441 QA pairs, 20% with Korean cultural context (Choi et al., 14 Sep 2025).
  • SCB: 1,065 images, 138 artifacts, five cultural categories, two-stage VLM evaluation (Satar et al., 20 Sep 2025).
  • EPiK: 181 binary-choice physics problems tied to Korean contexts (Jeong et al., 22 Sep 2025).

Typically, the inclusion of explicit location or cultural context (e.g., ℓ ∈ {none, region, country}) in the prompt improves LLM performance on culturally nuanced reasoning tasks.

4. Evaluation, Baselines, and Performance Analysis

Evaluation protocols are tailored to test not only correctness but cultural sensitivity and reasoning depth:

• Accuracy Scores: Human performance routinely exceeds 89–100% on culturally congruent datasets. By contrast, state-of-the-art LLMs and VLMs lag—accuracies of 53–83% are common depending on task and language (Talmor et al., 2018, Koto et al., 2 Apr 2024, Sadallah et al., 18 Feb 2025, Choi et al., 14 Sep 2025).
• Fine-grained Analysis: Models trained primarily on Western-centric data underperform on East Asian, South Asian, Arab, and low-resource language benchmarks (Yin et al., 2021, Ponti et al., 2020, Choi et al., 14 Sep 2025).
• Metric Design: Datasets such as CRIC (Gao et al., 2019) and SCB (Satar et al., 20 Sep 2025) incorporate dual metrics for answer selection and spatial grounding (mean IoU).
• Cultural Challenge Points: Items requiring explicit local knowledge (e.g., regional foods, attire, household artifacts) are more challenging and drive largest model accuracy gaps.

Dataset	Region/Language Scope	Task Format
ArabCulture	13 MENA countries	MCQ across 12 domains
IndoCulture	11 Indonesian provinces	Sentence-completion, MCQ
Ko-PIQA	Korean	MCQ, 20% cultural scenarios
SCB	7 SE Asian countries	Multi-stage VQA + segmentation
EPiK	Korean	Binary choice, physical reasoning

5. Model Adaptation and Cultural Alignment

Recent research reveals that lightweight alignment methods can enable cross-cultural transfer and recalibration of LLM outputs for culturally grounded tasks (Almheiri et al., 23 Sep 2025):

• In-context Learning (ICL): Supplying as few as 12 culturally specific demonstrations per country can yield improvements of 2–10% in target performance.
• Demonstration-based Iterative Task Tuning Optimization (DITTO): Reinforcement-style updates with minimal data efficiently realign model outputs, often matching or exceeding gains from supervised fine-tuning.
• Cross-cultural transfer: Surprisingly, alignment using out-of-culture demonstrations (e.g., IndoCulture, US) can match in-culture performance for MCQ tasks—indicating underlying transferability of certain commonsense knowledge.
• Low-resource language strategies: Synthetic generation via LLMs, validated through classifiers and human review, can bootstrap high-quality datasets outperforming machine translation (Pranida et al., 18 Feb 2025).

Training and adaptation protocols frequently rely on standard cross-entropy loss for classification, and custom learning rates/parameters for alignment (e.g., α, β in DPO). For clustering and filtering, IDF-like metrics and embedding-based cosine similarity measures are used.

6. Implications for AI Evaluation and Development

The proliferation of culturally grounded datasets is reshaping the landscape of commonsense reasoning evaluation and dataset design:

• Bias Mitigation: Datasets constructed from scratch by native experts (ArabCulture, IndoCulture) better reflect local norms and minimize Anglocentric contamination in both data and model performance assessment.
• Benchmarking for Multimodal and Physical Reasoning: Expanded benchmarks (SCB, GD-VCR, MessySurfaces, Ko-PIQA, EPiK) are essential as VLMs and robots interact with region-specific artifacts, spatial conventions, and physical solutions.
• Diagnosis and Model Robustness: Performance gaps (up to 30–40 points) highlight where systems fail to generalize authentically; design choices such as question complexity, distractor similarity, and explicit spatial grounding are key diagnostic levers.
• Towards Inclusive NLP and Robotics: These resources guide fine-tuning, evaluation, and deployment strategies for real-world AI applications sensitive to cultural context—virtual assistants, service robots, QA systems, dialogue agents.

7. Future Directions

Several open challenges and opportunities remain:

• Scaling: Extending coverage to more regions, dialects, and underrepresented cultures.
• Hybrid Knowledge Representations: Integrating graph-based knowledge (CANDLE, cultural commonsense graphs (Acharya et al., 2020)) with neural inferences.
• Dynamic and Multimodal Reasoning: Combining language, vision, and interaction pipelines for normatively varied tasks (active perception (Kwon et al., 2023)).
• Systematic Inclusion of Temporal and Societal Evolution: Capturing shifting norms and emergent cultural phenomena over time.
• Explanation and Transparency: Evaluation should include not only answer correctness but also justification of reasoning aligned with sociocultural practices.

Culturally grounded commonsense reasoning datasets represent a critical frontier in developing AI systems capable of nuanced, context-sensitive, and globally representative understanding, reasoning, and interaction. Their methodologies, scope, and evaluation frameworks continue to evolve, shaping the future of inclusive, culturally aware artificial intelligence research.