# Cultural Commonsense Knowledge for Intercultural Dialogues

arXiv: https://arxiv.org/abs/2402.10689 · HTML: https://mlsanity.com/paper/2402.10689/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Visually Grounded Reasoning across Languages and Cultures](https://mlsanity.com/paper/2109.13238/) (2021)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://mlsanity.com/paper/2212.10465/) (2022)
- [Multi-Dimensional Gender Bias Classification](https://mlsanity.com/paper/2005.00614/) (2020)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning](https://mlsanity.com/paper/2109.06860/) (2021)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly](https://mlsanity.com/paper/2210.08604/) (2022)
- [GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models](https://mlsanity.com/paper/2205.12247/) (2022)
- [Towards an Atlas of Cultural Commonsense for Machine Reasoning](https://mlsanity.com/paper/2009.05664/) (2020)
- [Commonsense Properties from Query Logs and Question Answering Forums](https://mlsanity.com/paper/1905.10989/) (2019)
- [Advanced Semantics for Commonsense Knowledge Extraction](https://mlsanity.com/paper/2011.00905/) (2020)
- [NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation](https://mlsanity.com/paper/2310.14563/) (2023)
- [Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment](https://mlsanity.com/paper/2305.14492/) (2023)
- [Cross-Cultural Transfer Learning for Chinese Offensive Language Detection](https://mlsanity.com/paper/2303.17927/) (2023)
- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes](https://mlsanity.com/paper/2205.14036/) (2022)
- [GD-COMET: A Geo-Diverse Commonsense Inference Model](https://mlsanity.com/paper/2310.15383/) (2023)
- [NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation](https://mlsanity.com/paper/2312.05979/) (2023)

## Built on by (most cited first, 20 of 20)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Enabling LLM Knowledge Analysis via Extensive Materialization](https://mlsanity.com/paper/2411.04920/) (2024)
- [Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration](https://mlsanity.com/paper/2505.20625/) (2025)
- [Do Large Language Models Truly Understand Cross-cultural Differences?](https://mlsanity.com/paper/2512.07075/) (2025)
- [SEADialogues: A Multilingual Culturally Grounded Multi-turn Dialogue Dataset on Southeast Asian Languages](https://mlsanity.com/paper/2508.07069/) (2025)
- [What are Foundation Models Cooking in the Post-Soviet World?](https://mlsanity.com/paper/2502.18583/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [Culture Cartography: Mapping the Landscape of Cultural Knowledge](https://mlsanity.com/paper/2510.27672/) (2025)
- [Multilingual Agent-Based World Modeling for Social Science](https://mlsanity.com/paper/2512.07195/) (2025)
- [CALM: Culturally Self-Aware Language Models](https://mlsanity.com/paper/2601.03483/) (2026)
- [ALIGN: Word Association Learning for Cultural Alignment in Large Language Models](https://mlsanity.com/paper/2508.13426/) (2025)
- [To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs](https://mlsanity.com/paper/2601.02858/) (2026)
- [LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction](https://mlsanity.com/paper/2601.17971/) (2026)
- [Conceptual Cultural Index: A Metric for Cultural Specificity via Relative Generality](https://mlsanity.com/paper/2602.09444/) (2026)
- [AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models](https://mlsanity.com/paper/2603.08275/) (2026)
- [ExCAM: Explainable Cultural Awareness Metrics](https://mlsanity.com/paper/2605.29897/) (2026)
- [GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base](https://mlsanity.com/paper/2608.06992/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)

## Abstract (arXiv)

Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents MANGO, a methodology for distilling high-accuracy, high-recall assertions of cultural knowledge. We judiciously and iteratively prompt LLMs for this purpose from two entry points, concepts and cultures. Outputs are consolidated via clustering and generative summarization. Running the MANGO method with GPT-3.5 as underlying LLM yields 167K high-accuracy assertions for 30K concepts and 11K cultures, surpassing prior resources by a large margin in quality and size. In an extrinsic evaluation for intercultural dialogues, we explore augmenting dialogue systems with cultural knowledge assertions. Notably, despite LLMs inherently possessing cultural knowledge, we find that adding knowledge from MANGO improves the overall quality, specificity, and cultural sensitivity of dialogue responses, as judged by human annotators. Data and code are available for download.