# Towards an Atlas of Cultural Commonsense for Machine Reasoning

arXiv: https://arxiv.org/abs/2009.05664 · HTML: https://mlsanity.com/paper/2009.05664/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?](https://mlsanity.com/paper/2210.15230/) (2022)
- [Extracting Cultural Commonsense Knowledge at Scale](https://mlsanity.com/paper/2210.07763/) (2022)
- [Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning](https://mlsanity.com/paper/2106.06937/) (2021)
- [Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning](https://mlsanity.com/paper/2109.06860/) (2021)
- [Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense](https://mlsanity.com/paper/2405.04655/) (2024)
- [GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models](https://mlsanity.com/paper/2205.12247/) (2022)
- ["I'm Not Mad": Commonsense Implications of Negation and Contradiction](https://mlsanity.com/paper/2104.06511/) (2021)
- [DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures](https://mlsanity.com/paper/2403.14651/) (2024)
- [Extrinsic Evaluation of Cultural Competence in Large Language Models](https://mlsanity.com/paper/2406.11565/) (2024)
- [SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://mlsanity.com/paper/2304.12026/) (2023)
- [Cultural Commonsense Knowledge for Intercultural Dialogues](https://mlsanity.com/paper/2402.10689/) (2024)
- [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)
- [Evaluating the Quality of Benchmark Datasets for Low-Resource Languages: A Case Study on Turkish](https://mlsanity.com/paper/2504.09714/) (2025)
- [CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts](https://mlsanity.com/paper/2410.15453/) (2024)
- [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://mlsanity.com/paper/2406.04215/) (2024)
- [CPopQA: Ranking Cultural Concept Popularity by LLMs](https://mlsanity.com/paper/2311.07897/) (2023)
- [NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain](https://mlsanity.com/paper/2310.10920/) (2023)
- [Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese](https://mlsanity.com/paper/2505.22645/) (2025)
- [WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain](https://mlsanity.com/paper/2408.11800/) (2024)
- [Towards Answering Open-ended Ethical Quandary Questions](https://mlsanity.com/paper/2205.05989/) (2022)
- [Finding Trolls Under Bridges: Preliminary Work on a Motif Detector](https://mlsanity.com/paper/2204.06085/) (2022)
- [Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry](https://mlsanity.com/paper/2411.03542/) (2024)
- [An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses](https://mlsanity.com/paper/2503.21393/) (2025)
- [PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model](https://mlsanity.com/paper/2310.07170/) (2023)
- [IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://mlsanity.com/paper/2312.06053/) (2023)
- [Everyday Physics in Korean Contexts: A Culturally Grounded Physical Reasoning Benchmark](https://mlsanity.com/paper/2509.17807/) (2025)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)

## Abstract (arXiv)

Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences. We introduce an approach that extends prior work on crowdsourcing commonsense knowledge by incorporating differences in knowledge that are attributable to cultural or national groups. We demonstrate the technique by collecting commonsense knowledge that surrounds six fairly universal rituals -- birth, coming-of-age, marriage, funerals, new year, and birthdays -- across two national groups: the United States and India. Our study expands the different types of relationships identified by existing work in the field of commonsense reasoning for commonplace events, and uses these new types to gather information that distinguish the identity of the groups providing the knowledge. It also moves us a step closer towards building a machine that doesn't assume a rigid framework of universal (and likely Western-biased) commonsense knowledge, but rather has the ability to reason in a contextually and culturally sensitive way. Our hope is that cultural knowledge of this sort will lead to more human-like performance in NLP tasks such as question answering (QA) and text understanding and generation.