---
title: "Common to Whom? Regional Cultural Commonsense and LLM"
description: "This paper tests whether that assumption holds for India and introduces INDICA for the purpose. 45 references."
authors: ["Sangmitra Madhusudan", "Trush Shashank More", "Steph Buongiorno", "Renata Dividino", "Jad Kabbara", "Ali Emami"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.249/"
doi: "10.18653/v1/2026.acl-long.249"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/madhusudan-2026-common-whom-regional-cultural-commonsense/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/madhusudan-2026-common-whom-regional-cultural-commonsense/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/) / Common to Whom? Regional Cultural Commonsense and LLM Bias in India

# Common to Whom? Regional Cultural Commonsense and LLM Bias in India

**Authors:** [Sangmitra Madhusudan](https://md.research.elamur.ai/free/en/authors/sangmitra-madhusudan/), Trush Shashank More, Steph Buongiorno, Renata Dividino, [Jad Kabbara](https://md.research.elamur.ai/free/en/authors/jad-kabbara/) and [Ali Emami](https://md.research.elamur.ai/free/en/authors/ali-emami/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.249/) · [PDF](https://aclanthology.org/2026.acl-long.249.pdf) · [DOI 10.18653/v1/2026.acl-long.249](https://doi.org/10.18653/v1/2026.acl-long.249) · [OpenAlex](https://openalex.org/W7166806257)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Benchmarks for cultural commonsense, such as CultureBank and CulturalBench, usually treat each country as a single cultural unit. This paper tests whether that assumption holds for India and introduces INDICA for the purpose. The questions come from the Outline of Cultural Materials (OCM) anthropological taxonomy, covering 8 everyday-life domains, 18 subcategories and 39 topics. GPT-4-0613 helped draft topics and questions, and the authors then filtered them by hand. Five Prolific participants from each of five regions (North, South, East, West, Central) answered 611 questions. A region receives a gold answer only when at least 4 of its 5 respondents agree. GPT-4o pre-classified agreement, and two annotators verified every case. The final set has 515 questions and 1,630 region-specific QA pairs. Only 39.4% of questions show agreement across all five regions, so everyday cultural knowledge in India is largely regional. Eight LLMs score just 13.4–20.9% on region-specific questions. When no region is specified, they favour Central and North Indian answers. The pipeline is offered as a template for other culturally diverse countries.

## Key points
- INDICA contains 515 questions and 1,630 region-specific answers. They were drawn from 15,275 raw responses (25 participants × 611 questions), and the dataset and code are released on GitHub and HuggingFace.
- Gold standards were set at three levels: within a region, between region pairs, and across all regions. The two human annotators agreed perfectly (Fleiss' κ = 1.0), and they overrode GPT-4o's preliminary labels in 7.6%, 28.9% and 24.5% of these cases respectively.
- Agreement between regions was judged strictly: partially overlapping answers, such as "silk" versus "silk and cotton," did not count as a match.
- Evaluation uses two task formats, RASA and RA-MCQ. Without regional context, models chose Central and North answers 30–40% more often than expected and under-represented East and West.
- The authors acknowledge several limitations:
  - the five-region scheme hides diversity within each region;
  - the Prolific sample likely skews toward English-speaking, online, urban respondents;
  - the data is a 2025 snapshot;
  - variation by religion, caste, gender and rural/urban residence is not analysed.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 211 works · 69,000 citations
- [Emory University](https://md.research.elamur.ai/free/en/universities/emory-university/) — University, Atlanta, United States · 36 works · 39,779 citations
- [Brock University](https://md.research.elamur.ai/free/en/universities/brock-university/) — University, St. Catharines, Canada · 3 works

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [🤝 Safety Research](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · 543 works

🧭 Topics (2): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works · [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno, Renata Dividino, Jad Kabbara and Ali Emami. 2026. Common to Whom? Regional Cultural Commonsense and LLM Bias in India. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{madhusudan2026common,
  title = {{Common to Whom? Regional Cultural Commonsense and LLM Bias in India}},
  author = {Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno, Renata Dividino, Jad Kabbara and Ali Emami},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.249},
  url = {https://doi.org/10.18653/v1/2026.acl-long.249}
}
```

- [← previous Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in LLMs](https://md.research.elamur.ai/free/en/papers/purkayastha-2026-common-sense-vs-morality-curious/)
- [next → CommonMorph: Participatory Morphological Documentation Platform](https://md.research.elamur.ai/free/en/papers/mahmudi-2026-commonmorph-participatory-morphological-documentation-platform/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/)

## References

45 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. 1950. The constitution of India. Eighth Schedule. [find](https://scholar.google.com/scholar?q=1950%2E+The+constitution+of+India%2E+Eighth+Schedule%2E)
2. Christabel Acquaye, Haozhe An, and Rachel Rudinger. 2024. Susu box or piggy bank: Assessing cultural commonsense knowledge between ghana and the US. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9483–9502, Miami, Florida, USA. Association for Comput… [find](https://scholar.google.com/scholar?q=Christabel+Acquaye%2C+Haozhe+An%2C+and+Rachel+Rudinger%2E+2024%2E+Susu+box+or+piggy+bank%3A+Assessing+cultural+commonsense+knowledge+between+ghana+and+the+US%2E+In+Proceedings+of+the+2024+Conference+on+Empirical+)
3. Meta AI. 2024. Llama 3.3: Advancing state-of-the-art in open foundation models. Technical report, Meta. [find](https://scholar.google.com/scholar?q=Meta+AI%2E+2024%2E+Llama+3%2E3%3A+Advancing+state%2Dof%2Dthe%2Dart+in+open+foundation+models%2E+Technical+report%2C+Meta%2E)
4. Mistral AI. 2025. Mistral large 3 (2512): A new benchmark for open-weight generalists. Technical report, Mistral AI. [find](https://scholar.google.com/scholar?q=Mistral+AI%2E+2025%2E+Mistral+large+3+%282512%29%3A+A+new+benchmark+for+open%2Dweight+generalists%2E+Technical+report%2C+Mistral+AI%2E)
5. Anthropic. 2025. Claude sonnet 4.5 technical overview. Technical report, Anthropic. [find](https://scholar.google.com/scholar?q=Anthropic%2E+2025%2E+Claude+sonnet+4%2E5+technical+overview%2E+Technical+report%2C+Anthropic%2E)
6. Nishant Balepur, Rachel Rudinger, and Jordan Lee Boyd-Graber. 2025. [Which of these best describes multiple choice evaluation with LLMs? a) forced B) flawed C) fixable D) all of the above](https://md.research.elamur.ai/free/en/papers/balepur-2025-which-these-best-describes-multiple/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Paper…
7. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020. [Piqa: Reasoning about physical commonsense in natural language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/). In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 7432–7439.
8. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curr…
9. Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, and Yejin Choi. 2025. [CulturalBench: A robust, diverse and challenging benchmark for measuring LMs’ cultural knowledge through human-AI red-teami…](https://md.research.elamur.ai/free/en/papers/chiu-2025-culturalbench-robust-diverse-challenging-benchmark/)
10. Ernest Davis and Gary Marcus. 2015. [Commonsense reasoning and commonsense knowledge in artificial intelligence](https://md.research.elamur.ai/free/en/papers/davis-2015-commonsense-reasoning-commonsense-knowledge-artificial/). Communications of the ACM, 58(9):92– 103.
11. Google DeepMind. 2025. Gemini 3 flash: Frontier intelligence at scale. Technical report, Google. [find](https://scholar.google.com/scholar?q=Google+DeepMind%2E+2025%2E+Gemini+3+flash%3A+Frontier+intelligence+at+scale%2E+Technical+report%2C+Google%2E)
12. DeepSeek-AI. 2025. Deepseek-v3.2: Integrating sparse attention with enhanced reasoning. Technical report, DeepSeek. [find](https://scholar.google.com/scholar?q=DeepSeek%2DAI%2E+2025%2E+Deepseek%2Dv3%2E2%3A+Integrating+sparse+attention+with+enhanced+reasoning%2E+Technical+report%2C+DeepSeek%2E)
13. Quyet V. Do, Junze Li, Tung-Duong Vuong, Zhaowei Wang, Yangqiu Song, and Xiaojuan Ma. 2024. What really is commonsense knowledge? Preprint, arXiv:2411.03964. — [arXiv:2411.03964](https://arxiv.org/abs/2411.03964) [find](https://scholar.google.com/scholar?q=Quyet+V%2E+Do%2C+Junze+Li%2C+Tung%2DDuong+Vuong%2C+Zhaowei+Wang%2C+Yangqiu+Song%2C+and+Xiaojuan+Ma%2E+2024%2E+What+really+is+commonsense+knowledge%3F+Preprint%2C+arXiv%3A2411%2E03964%2E+%E2%80%94+%5BarXiv%3A2411%2E03964%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs)
14. Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul N.C., Avik Bhattacharyya, Mitesh M. Khapra, and Pratyush Kumar. 2020. [IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/). In Findings of the Association for Computation…
15. Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023. [Large language models struggle to learn long-tail knowledge](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/). In Proceedings of the 40th International Conference on Machine Learning, volume 202 of ICML’23, pages 15696–15707. PMLR.
16. Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, and Aida Nematzadeh. 2022. A systematic investigation of commonsense knowledge in large language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11… [find](https://scholar.google.com/scholar?q=Xiang+Lorraine+Li%2C+Adhiguna+Kuncoro%2C+Jordan+Hoffmann%2C+Cyprien+de+Masson+d%E2%80%99Autume%2C+Phil+Blunsom%2C+and+Aida+Nematzadeh%2E+2022%2E+A+systematic+investigation+of+commonsense+knowledge+in+large+language+models%2E)
17. Aniket Maji and 1 others. 2025. Sanskriti: A comprehensive benchmark for evaluating language models’ knowledge of indian culture. Preprint, arXiv:2506.15355. [find](https://scholar.google.com/scholar?q=Aniket+Maji+and+1+others%2E+2025%2E+Sanskriti%3A+A+comprehensive+benchmark+for+evaluating+language+models%E2%80%99+knowledge+of+indian+culture%2E+Preprint%2C+arXiv%3A2506%2E15355%2E)
18. Tarek Naous, Anagha Savit, Carlos Rafael Catalan, Geyang Guo, Jaehyeok Lee, Kyungdon Lee, Lheane Marie Dizon, Mengyu Ye, Neel Kothari, Sahajpreet Singh, Sarah Masud, Tanish Patwa, Trung Thanh Tran, Zohaib Khan, Alan Ritter, JinYeong Bak, Keisuke Sakaguchi, Tanmoy Chakraborty, Yuki Arase, and Wei Xu.… — [arXiv:2510.05291](https://arxiv.org/abs/2510.05291) [find](https://scholar.google.com/scholar?q=Tarek+Naous%2C+Anagha+Savit%2C+Carlos+Rafael+Catalan%2C+Geyang+Guo%2C+Jaehyeok+Lee%2C+Kyungdon+Lee%2C+Lheane+Marie+Dizon%2C+Mengyu+Ye%2C+Neel+Kothari%2C+Sahajpreet+Singh%2C+Sarah+Masud%2C+Tanish+Patwa%2C+Trung+Thanh+Tran%2C+Zo)
19. Janki Atul Nawale, Mohammed Safi Ur Rahman Khan, Janani D, Mansi Gupta, Danish Pruthi, and Mitesh M Khapra. 2025. FairI tales: Evaluation of fairness in Indian contexts with a focus on bias and stereotypes. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (V… [find](https://scholar.google.com/scholar?q=Janki+Atul+Nawale%2C+Mohammed+Safi+Ur+Rahman+Khan%2C+Janani+D%2C+Mansi+Gupta%2C+Danish+Pruthi%2C+and+Mitesh+M+Khapra%2E+2025%2E+FairI+tales%3A+Evaluation+of+fairness+in+Indian+contexts+with+a+focus+on+bias+and+stereo)
20. Tuan-Phong Nguyen and 1 others. 2023. Extracting cultural commonsense knowledge at scale. In WWW. [find](https://scholar.google.com/scholar?q=Tuan%2DPhong+Nguyen+and+1+others%2E+2023%2E+Extracting+cultural+commonsense+knowledge+at+scale%2E+In+WWW%2E)
21. OpenAI. 2025. Gpt-5.2 technical report. Technical report, OpenAI. [find](https://scholar.google.com/scholar?q=OpenAI%2E+2025%2E+Gpt%2D5%2E2+technical+report%2E+Technical+report%2C+OpenAI%2E)
22. Shramay Palta and Rachel Rudinger. 2023. [FORK: A bite-sized test set for probing culinary cultural biases in commonsense reasoning models](https://md.research.elamur.ai/free/en/papers/palta-2023-fork-bite-sized-test-set/). In Findings of the Association for Computational Linguistics: ACL 2023, pages 9952–9962, Toronto, Canada. Association for Computational Linguistics.
23. Gopal K. Patidar and Yashaswi Dhiman. 2021. Distribution of abo and rh (d) blood groups in india: A systematic review. ISBT Science Series, 16(1):37– 48. [find](https://scholar.google.com/scholar?q=Gopal+K%2E+Patidar+and+Yashaswi+Dhiman%2E+2021%2E+Distribution+of+abo+and+rh+%28d%29+blood+groups+in+india%3A+A+systematic+review%2E+ISBT+Science+Series%2C+16%281%29%3A37%E2%80%93+48%2E)
24. Abhinav Sukumar Rao, Akhila Yerukola, Vishwa Shah, Katharina Reinecke, and Maarten Sap. 2025. [NormAd: A framework for measuring the cultural adaptability of large language models](https://md.research.elamur.ai/free/en/papers/rao-2025-normad-framework-measuring-cultural-adaptability/). In Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the ACL. Association for Computat…
25. Pritika Rohera, Chaitrali Ginimav, Akanksha Salunke, Gayatri Sawant, and Raviraj Joshi. 2024. L3cube-indicquest: A benchmark question answering dataset for evaluating knowledge of llms in indic context. Preprint, arXiv:2409.08706. [find](https://scholar.google.com/scholar?q=Pritika+Rohera%2C+Chaitrali+Ginimav%2C+Akanksha+Salunke%2C+Gayatri+Sawant%2C+and+Raviraj+Joshi%2E+2024%2E+L3cube%2Dindicquest%3A+A+benchmark+question+answering+dataset+for+evaluating+knowledge+of+llms+in+indic+contex)
26. Snehasis Mukhopadhyay, Aryan Kasat, Shivam Dubey, Rahul Karthikeyan, Dhruv Sood, Vinija Jain, Aman Chadha, and Amitava Das. 2025. Ambedkar-a multi-level bias elimination through a decoding approach with knowledge augmentation for robust constitutional alignment of language models. Preprint, arXiv:25… — [arXiv:2509.02133](https://arxiv.org/abs/2509.02133) [find](https://scholar.google.com/scholar?q=Snehasis+Mukhopadhyay%2C+Aryan+Kasat%2C+Shivam+Dubey%2C+Rahul+Karthikeyan%2C+Dhruv+Sood%2C+Vinija+Jain%2C+Aman+Chadha%2C+and+Amitava+Das%2E+2025%2E+Ambedkar%2Da+multi%2Dlevel+bias+elimination+through+a+decoding+approach+wi)
27. Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, and Iryna Gurevych. 2021. [How good is your tokenizer? on the monolingual performance of multilingual language models](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/). In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Join…
28. George P. Murdock, Clellan S. Ford, Alfred E. Hudson, Raymond Kennedy, Leo W. Simmons, and John W. M. Whiting. 2008. Outline of Cultural Materials, 6th revised edition with modifications edition. Human Relations Area Files, New Haven. [find](https://scholar.google.com/scholar?q=George+P%2E+Murdock%2C+Clellan+S%2E+Ford%2C+Alfred+E%2E+Hudson%2C+Raymond+Kennedy%2C+Leo+W%2E+Simmons%2C+and+John+W%2E+M%2E+Whiting%2E+2008%2E+Outline+of+Cultural+Materials%2C+6th+revised+edition+with+modifications+edition%2E+Huma)
29. Nihar Sahoo, Pranamya Kulkarni, Arif Ahmad, Tanu Goyal, Narjis Asad, Aparna Garimella, and Pushpak Bhattacharyya. 2024. IndiBias: A benchmark dataset to measure social biases in language models for Indian context. In Proceedings of the 2024 Conference of the North American Chapter of the Association… [find](https://scholar.google.com/scholar?q=Nihar+Sahoo%2C+Pranamya+Kulkarni%2C+Arif+Ahmad%2C+Tanu+Goyal%2C+Narjis+Asad%2C+Aparna+Garimella%2C+and+Pushpak+Bhattacharyya%2E+2024%2E+IndiBias%3A+A+benchmark+dataset+to+measure+social+biases+in+language+models+for+In)
30. Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021. Winogrande: An adversarial Winograd schema challenge at scale. In Communications of the ACM, volume 64, pages 99– 106. ACM New York, NY, USA. [find](https://scholar.google.com/scholar?q=Keisuke+Sakaguchi%2C+Ronan+Le+Bras%2C+Chandra+Bhagavatula%2C+and+Yejin+Choi%2E+2021%2E+Winogrande%3A+An+adversarial+Winograd+schema+challenge+at+scale%2E+In+Communications+of+the+ACM%2C+volume+64%2C+pages+99%E2%80%93+106%2E+ACM+)
31. K. J. Sankalp, Ashutosh Kumar, Laxmaan Balaji, Nikunj Kotecha, Vinija Jain, Aman Chadha, and Sreyoshi Bhaduri. 2025. IndicMMLU-Pro: Benchmarking Indic large language models on multi-task language understanding. Preprint, arXiv:2501.15747. [find](https://scholar.google.com/scholar?q=K%2E+J%2E+Sankalp%2C+Ashutosh+Kumar%2C+Laxmaan+Balaji%2C+Nikunj+Kotecha%2C+Vinija+Jain%2C+Aman+Chadha%2C+and+Sreyoshi+Bhaduri%2E+2025%2E+IndicMMLU%2DPro%3A+Benchmarking+Indic+large+language+models+on+multi%2Dtask+language+unde)
32. Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, and Yejin Choi. 2019a. Atomic: An atlas of machine commonsense for if-then reasoning. In AAAI. [find](https://scholar.google.com/scholar?q=Maarten+Sap%2C+Ronan+Le+Bras%2C+Emily+Allaway%2C+Chandra+Bhagavatula%2C+Nicholas+Lourie%2C+and+Yejin+Choi%2E+2019a%2E+Atomic%3A+An+atlas+of+machine+commonsense+for+if%2Dthen+reasoning%2E+In+AAAI%2E)
33. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019b. [Socialiqa: Commonsense reasoning about social interactions](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/). In EMNLP.
34. Agrima Seth, Sanchit Ahuja, Kalika Bali, and Sunayana Sitaram. 2024. [DOSA: A dataset of social artifacts from different indian geographical subcultures](https://md.research.elamur.ai/free/en/papers/seth-2024-dosa-dataset-social-artifacts-different/). In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages…
35. Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. Conceptnet 5.5: An open multilingual graph of general knowledge. In AAAI. [find](https://scholar.google.com/scholar?q=Robyn+Speer%2C+Joshua+Chin%2C+and+Catherine+Havasi%2E+2017%2E+Conceptnet+5%2E5%3A+An+open+multilingual+graph+of+general+knowledge%2E+In+AAAI%2E)
36. Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. [Commonsenseqa: A question answering challenge targeting commonsense knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/). In NAACL.
37. Qwen Team. 2025. [Qwen3-vl technical report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/). arXiv preprint arXiv:2511.21631.
38. Fons J. R. Van de Vijver and Kwok Leung. 1997. Methods and Data Analysis for Cross-Cultural Research. SAGE Publications, Thousand Oaks, CA. [find](https://scholar.google.com/scholar?q=Fons+J%2E+R%2E+Van+de+Vijver+and+Kwok+Leung%2E+1997%2E+Methods+and+Data+Analysis+for+Cross%2DCultural+Research%2E+SAGE+Publications%2C+Thousand+Oaks%2C+CA%2E)
39. Sshubam Verma and 1 others. 2025. Milu: A multi-task indic language understanding benchmark. In NAACL. [find](https://scholar.google.com/scholar?q=Sshubam+Verma+and+1+others%2E+2025%2E+Milu%3A+A+multi%2Dtask+indic+language+understanding+benchmark%2E+In+NAACL%2E)
40. Amber Wutich, Gery Ryan, and H. Russell Bernard. 2014. Text analysis. In H. Russell Bernard, editor, Handbook of Methods in Cultural Anthropology, 2nd edition, pages 155–188. Rowman & Littlefield, Lanham, MD. xAI. 2025. Grok-4 fast: Unified reasoning and inference. Technical report, xAI. [find](https://scholar.google.com/scholar?q=Amber+Wutich%2C+Gery+Ryan%2C+and+H%2E+Russell+Bernard%2E+2014%2E+Text+analysis%2E+In+H%2E+Russell+Bernard%2C+editor%2C+Handbook+of+Methods+in+Cultural+Anthropology%2C+2nd+edition%2C+pages+155%E2%80%93188%2E+Rowman+%26+Littlefield%2C+Lan)
41. Da Yin, Jack Hessel, and 1 others. 2022. Geomlama: Geo-diverse commonsense probing on multilingual pre-trained language models. In EMNLP. [find](https://scholar.google.com/scholar?q=Da+Yin%2C+Jack+Hessel%2C+and+1+others%2E+2022%2E+Geomlama%3A+Geo%2Ddiverse+commonsense+probing+on+multilingual+pre%2Dtrained+language+models%2E+In+EMNLP%2E)
42. Hari Shankar, Vedanta S P, Tejas Cavale, Ponnurangam Kumaraguru, and Abhijnan Chakraborty. 2025. Sometimes the model doth preach: Quantifying religious bias in open llms through demographic analysis in asian nations. Preprint, arXiv:2503.07510. — [arXiv:2503.07510](https://arxiv.org/abs/2503.07510) [find](https://scholar.google.com/scholar?q=Hari+Shankar%2C+Vedanta+S+P%2C+Tejas+Cavale%2C+Ponnurangam+Kumaraguru%2C+and+Abhijnan+Chakraborty%2E+2025%2E+Sometimes+the+model+doth+preach%3A+Quantifying+religious+bias+in+open+llms+through+demographic+analysis+i)
43. Siqi Shen, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Soujanya Poria, and Rada Mihalcea. 2024. Understanding the capabilities and limitations of large language models for cultural commonsense. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computation… [find](https://scholar.google.com/scholar?q=Siqi+Shen%2C+Lajanugen+Logeswaran%2C+Moontae+Lee%2C+Honglak+Lee%2C+Soujanya+Poria%2C+and+Rada+Mihalcea%2E+2024%2E+Understanding+the+capabilities+and+limitations+of+large+language+models+for+cultural+commonsense%2E+In)
44. Weiyan Shi, Ryan Li, Yutong Zhang, Caleb Ziems, Sunny Yu, Raya Horesh, Rogério Abreu De Paula, and Diyi Yang. 2024. [CultureBank: An online community-driven knowledge base towards culturally aware language technologies](https://md.research.elamur.ai/free/en/papers/shi-2024-culturebank-online-community-driven-knowledge/). In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4…
45. Urmila Sinha, Shiv Kumar Mudgal, Ajay Kumar Patel, Vipin Patidar, and Sanjay Kumar. 2025. Mapping the [find](https://scholar.google.com/scholar?q=Urmila+Sinha%2C+Shiv+Kumar+Mudgal%2C+Ajay+Kumar+Patel%2C+Vipin+Patidar%2C+and+Sanjay+Kumar%2E+2025%2E+Mapping+the)

*Updated 1 November 2026*