---
title: "DRISHTIKON — paper by Maji et al. (2025) — summary and ideas"
description: "Current vision-language models handle many languages fluently but often miss culturally specific… 71 references, cited by 6 papers here."
authors: ["Arijit Maji", "Raghvendra Kumar", "Akash Ghosh", "Anushka", "Nemil Shah", "Abhilekh Borah", "Vanshika Shah", "Nishant Mishra", "Sriparna Saha"]
year: 2025
venue: "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2025.emnlp-main.68/"
doi: "10.18653/v1/2025.emnlp-main.68"
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/maji-2025-drishtikon-multimodal-multilingual-benchmark-testing/"
---
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# DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture

**Authors:** Arijit Maji, Raghvendra Kumar, [Akash Ghosh](https://md.research.elamur.ai/free/en/authors/akash-ghosh/), Anushka, Nemil Shah, Abhilekh Borah, Vanshika Shah, [Nishant Mishra](https://md.research.elamur.ai/free/en/authors/nishant-mishra/) and [Sriparna Saha](https://md.research.elamur.ai/free/en/authors/sriparna-saha/) · **Year:** 2025 · **Venue:** [Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/)) · **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/2025.emnlp-main.68/) · [PDF](https://aclanthology.org/2025.emnlp-main.68.pdf) · [DOI 10.18653/v1/2025.emnlp-main.68](https://doi.org/10.18653/v1/2025.emnlp-main.68) · [OpenAlex](https://openalex.org/W4416037238)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/maji-2025-drishtikon-multimodal-multilingual-benchmark-testing/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Current vision-language models handle many languages fluently but often miss culturally specific meaning. Existing cultural benchmarks also cover India only superficially, without combining multiple Indian languages, images and region-level detail. DRISHTIKON addresses this gap. It is a multiple-choice benchmark about Indian culture in which every question is paired with an image. The authors began with 2,126 English questions drawn from authoritative sources such as national repositories and state tourism portals. They spread these proportionally over every state and union territory, covering themes like festivals, attire, cuisine, art and monuments. They then expanded the questions with reasoning-oriented variants and translated them, reaching 64,288 instances in 15 languages. Small, large, proprietary, reasoning-focused and Indic-specific VLMs were tested with zero-shot and chain-of-thought prompting. GPT-4o-mini led overall, while some compact models proved surprisingly competitive. Chain-of-thought helped most on multi-hop and analogy questions. Low-resource languages and less-documented traditions remained clearly harder. The resource matters as a testbed for culturally inclusive, multilingual multimodal AI.

## Key points
- **Scale and coverage:** 64,288 image-question instances in 15 languages (English included), with each of the 28 states and 8 union territories represented.
- **Question design:** each question has four options with one correct answer. The three distractors were built in a semi-automated way and then human-curated: one close to the answer, one reflecting a common misconception, and one superficially similar. Each question went through two validation passes.
- **Models evaluated:** SmolVLM-256M-Instruct, InternVL3-1B/14B, Janus-Pro-7B, Qwen2-VL-7B-Instruct, Qwen2.5-Omni-7B, Llama-4-Scout-17B-16E-Instruct, LLaVA-1.6-Mistral-7B, Gemma-3-27B-IT, GPT-4o-mini, Kimi-VL-A3B-Thinking, Chitrarth and Maya.
- **Main findings:**
  - Chain-of-thought raised accuracy by roughly 10–15% on reasoning-heavy question types.
  - Kimi-VL-A3B-Thinking and Chitrarth gained little or inconsistently from chain-of-thought.
  - Languages such as Konkani and Sindhi lag behind Hindi and Bengali.
  - Among Indian-developed models, Maya performed well.
- **Licensing:** the benchmark is restricted to research and non-commercial use. Images are distributed only as URLs rather than files.

*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

- [Indian Institute of Technology Patna](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-patna/) — University, Patna, India · 21 works · 374 citations
- [Manipal University Jaipur](https://md.research.elamur.ai/free/en/universities/manipal-university-jaipur/) — University, Jaipur, India · 4 works · 265 citations
- [Dwarkadas J. Sanghvi College of Engineering](https://md.research.elamur.ai/free/en/universities/dwarkadas-j-sanghvi-college-of-engineering/) — University, Mumbai, India · 3 works · 9 citations

Also on the byline: [Pandit Deendayal Energy University](https://ror.org/02nsv5p42) · [Banasthali University](https://ror.org/05ycegt40).

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## Cite

Cite as: Arijit Maji, Raghvendra Kumar, Akash Ghosh, Anushka, Nemil Shah, Abhilekh Borah, Vanshika Shah, Nishant Mishra and Sriparna Saha. 2025. DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{maji2025drishtikon,
  title = {{DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture}},
  author = {Arijit Maji, Raghvendra Kumar, Akash Ghosh, Anushka, Nemil Shah, Abhilekh Borah, Vanshika Shah, Nishant Mishra and Sriparna Saha},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.68},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.68}
}
```

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## References

71 references, 30 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Shivdutt Singh, Alham Fikri Aji, Jacki O’Neill, Ashutosh Modi, and Monojit Choudhury. 2024. [Towards measuring and modeling “culture” in LLMs: A survey](https://md.research.elamur.ai/free/en/papers/adilazuarda-2024-measuring-modeling-culture-llms-survey/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Lan…
3. Meta AI. 2025. Llama-4-scout-17b-16e-instruct. [https://huggingface.co/meta-llama/](https://huggingface.co/meta-llama/) Llama-4-Scout-17B-16E-Instruct. Accessed: 2025-05-19. [↗](https://huggingface.co/meta-llama/)
4. Nahid Alam, Karthik Reddy Kanjula, Surya Guthikonda, Timothy Chung, Bala Krishna S Vegesna, Abhipsha Das, Anthony Susevski, Ryan Sze-Yin Chan, S M Iftekhar Uddin, Shayekh Bin Islam, Roshan Santhosh, Snegha A, Drishti Sharma, Chen Liu, Isha Chaturvedi, Genta Indra Winata, Ashvanth. S, Snehanshu Mukhe… — [arXiv:2412.07112](https://arxiv.org/abs/2412.07112) [find](https://scholar.google.com/scholar?q=Nahid+Alam%2C+Karthik+Reddy+Kanjula%2C+Surya+Guthikonda%2C+Timothy+Chung%2C+Bala+Krishna+S+Vegesna%2C+Abhipsha+Das%2C+Anthony+Susevski%2C+Ryan+Sze%2DYin+Chan%2C+S+M+Iftekhar+Uddin%2C+Shayekh+Bin+Islam%2C+Roshan+Santhosh%2C+S)
5. Badr AlKhamissi, Muhammad ElNokrashy, Mai AlKhamissi, and Mona Diab. 2024. [Investigating cultural alignment of large language models](https://md.research.elamur.ai/free/en/papers/alkhamissi-2024-investigating-cultural-alignment-large-language/). arXiv preprint arXiv:2402.13231.
6. Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. 2020. [On the cross-lingual transferability of monolingual representations](https://md.research.elamur.ai/free/en/papers/artetxe-2020-cross-lingual-transferability-monolingual-representations/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4623–4637, Online. Association for Computational Linguistics.
7. Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023. [Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/). arXiv preprint arXiv:2308.12966.
8. Shaily Bhatt, Sunipa Dev, Partha Talukdar, Shachi Dave, and Vinodkumar Prabhakaran. 2022. Cultural re-contextualization of fairness research in language technologies in india. arXiv preprint arXiv:2211.11206. — [arXiv:2211.11206](https://arxiv.org/abs/2211.11206) [find](https://scholar.google.com/scholar?q=Shaily+Bhatt%2C+Sunipa+Dev%2C+Partha+Talukdar%2C+Shachi+Dave%2C+and+Vinodkumar+Prabhakaran%2E+2022%2E+Cultural+re%2Dcontextualization+of+fairness+research+in+language+technologies+in+india%2E+arXiv+preprint+arXiv%3A221)
9. Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. [Language (technology) is power: A critical survey of “bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5454– 5476, Online. Association for Computational Linguistics.
10. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… — [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
11. Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. 2025. [Janus-pro: Unified multimodal understanding and generation with data and model scaling](https://md.research.elamur.ai/free/en/papers/chen-2025-janus-pro-unified-multimodal/). arXiv preprint arXiv:2501.17811.
12. Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, and 1 others. 2024a. [Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/). arXiv preprint arXiv:2412.05271.
13. Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24185–24198. Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, M… — [arXiv:2410.02677](https://arxiv.org/abs/2410.02677) [find](https://scholar.google.com/scholar?q=Scaling+up+vision+foundation+models+and+aligning+for+generic+visual%2Dlinguistic+tasks%2E+In+Proceedings+of+the+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition%2C+pages+24185%E2%80%9324198%2E+Yu+Ying+C)
14. Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. [TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages](https://md.research.elamur.ai/free/en/papers/clark-2020-tydi-qa-benchmark-information-seeking/). Transactions of the Association for Computational Linguistics, 8:454–470…
15. Marta R Costa-Jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, and 1 others. 2022. No language left behind: Scaling human-centered machine translation. arXiv preprint arXiv:2207.04672. [find](https://scholar.google.com/scholar?q=Marta+R+Costa%2DJuss%C3%A0%2C+James+Cross%2C+Onur+%C3%87elebi%2C+Maha+Elbayad%2C+Kenneth+Heafield%2C+Kevin+Heffernan%2C+Elahe+Kalbassi%2C+Janice+Lam%2C+Daniel+Licht%2C+Jean+Maillard%2C+and+1+others%2E+2022%2E+No+language+left+behind%3A+Sc)
16. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
17. Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, Shreya Goyal, Mitesh M. Khapra, Anoop Kunchukuttan, and Pratyush Kumar. 2023. [Towards leaving no Indic language behind: Building monolingual corpora, benchmark and models for Indic languages](https://md.research.elamur.ai/free/en/papers/doddapaneni-2023-leaving-no-indic-language-behind/). In Proceedings of the 61st Annual Meeting of the Asso…
18. Esin Durmus, Karina Nguyen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, and 1 others. 2023. [Towards measuring the representation of subjective global opinions in language models](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/). arXiv preprint arXiv:2306.16388.
19. Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, and 1 others. 2024b. [How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/). arXiv preprint arXiv:2404.16821.
20. Akash Ghosh, Arkadeep Acharya, Prince Jha, Sriparna Saha, Aniket Gaudgaul, Rajdeep Majumdar, Aman Chadha, Raghav Jain, Setu Sinha, and Shivani Agarwal. 2024a. Medsumm: A multimodal approach to summarizing code-mixed hindi-english clinical queries. In European Conference on Information Retrieval, pag… [find](https://scholar.google.com/scholar?q=Akash+Ghosh%2C+Arkadeep+Acharya%2C+Prince+Jha%2C+Sriparna+Saha%2C+Aniket+Gaudgaul%2C+Rajdeep+Majumdar%2C+Aman+Chadha%2C+Raghav+Jain%2C+Setu+Sinha%2C+and+Shivani+Agarwal%2E+2024a%2E+Medsumm%3A+A+multimodal+approach+to+summari)
21. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, and 1 others. 2024c. [find](https://scholar.google.com/scholar?q=Zhe+Chen%2C+Jiannan+Wu%2C+Wenhai+Wang%2C+Weijie+Su%2C+Guo+Chen%2C+Sen+Xing%2C+Muyan+Zhong%2C+Qinglong+Zhang%2C+Xizhou+Zhu%2C+Lewei+Lu%2C+and+1+others%2E+2024c%2E)
22. Internvl: Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Vinija Jain, and Aman Chadha. 2024b. Exploring the frontier of vision-language models: A survey of current methodologies and future directions. arXiv preprint arXiv:2404.07214. — [arXiv:2404.07214](https://arxiv.org/abs/2404.07214) [find](https://scholar.google.com/scholar?q=Internvl%3A+Akash+Ghosh%2C+Arkadeep+Acharya%2C+Sriparna+Saha%2C+Vinija+Jain%2C+and+Aman+Chadha%2E+2024b%2E+Exploring+the+frontier+of+vision%2Dlanguage+models%3A+A+survey+of+current+methodologies+and+future+directions%2E+)
23. Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Gaurav Pandey, Dinesh Raghu, and Setu Sinha. 2024c. Healthalignsumm: Utilizing alignment for multimodal summarization of code-mixed healthcare dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11546– 11560. [find](https://scholar.google.com/scholar?q=Akash+Ghosh%2C+Arkadeep+Acharya%2C+Sriparna+Saha%2C+Gaurav+Pandey%2C+Dinesh+Raghu%2C+and+Setu+Sinha%2E+2024c%2E+Healthalignsumm%3A+Utilizing+alignment+for+multimodal+summarization+of+code%2Dmixed+healthcare+dialogues%2E+)
24. Akash Ghosh, Debayan Datta, Sriparna Saha, and Chirag Agarwal. 2025. The multilingual mind: A survey of multilingual reasoning in language models. arXiv preprint arXiv:2502.09457. [find](https://scholar.google.com/scholar?q=Akash+Ghosh%2C+Debayan+Datta%2C+Sriparna+Saha%2C+and+Chirag+Agarwal%2E+2025%2E+The+multilingual+mind%3A+A+survey+of+multilingual+reasoning+in+language+models%2E+arXiv+preprint+arXiv%3A2502%2E09457%2E)
25. Akash Ghosh, Mohit Tomar, Abhisek Tiwari, Sriparna Saha, Jatin Salve, and Setu Sinha. 2024d. From sights to insights: Towards summarization of multimodal clinical documents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1311… [find](https://scholar.google.com/scholar?q=Akash+Ghosh%2C+Mohit+Tomar%2C+Abhisek+Tiwari%2C+Sriparna+Saha%2C+Jatin+Salve%2C+and+Setu+Sinha%2E+2024d%2E+From+sights+to+insights%3A+Towards+summarization+of+multimodal+clinical+documents%2E+In+Proceedings+of+the+62nd)
26. Google DeepMind. 2025. Gemini pro. [https://deepmind.google/technologies/gemini/pro/](https://deepmind.google/technologies/gemini/pro/). Accessed: 2025-05-19. [↗](https://deepmind.google/technologies/gemini/pro/)
27. Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2021. The flores-101 evaluation benchmark for low-resource and multilingual machine translation. [find](https://scholar.google.com/scholar?q=Naman+Goyal%2C+Cynthia+Gao%2C+Vishrav+Chaudhary%2C+Peng%2DJen+Chen%2C+Guillaume+Wenzek%2C+Da+Ju%2C+Sanjana+Krishnan%2C+Marc%E2%80%99Aurelio+Ranzato%2C+Francisco+Guzm%C3%A1n%2C+and+Angela+Fan%2E+2021%2E+The+flores%2D101+evaluation+benchmark)
28. Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019. Two new evaluation datasets for low-resource machine translation: Nepali-english and sinhala-english. [find](https://scholar.google.com/scholar?q=Francisco+Guzm%C3%A1n%2C+Peng%2DJen+Chen%2C+Myle+Ott%2C+Juan+Pino%2C+Guillaume+Lample%2C+Philipp+Koehn%2C+Vishrav+Chaudhary%2C+and+Marc%E2%80%99Aurelio+Ranzato%2E+2019%2E+Two+new+evaluation+datasets+for+low%2Dresource+machine+translati)
29. Md Arid Hasan, Maram Hasanain, Fatema Ahmad, Sahinur Rahman Laskar, Sunaya Upadhyay, Vrunda N Sukhadia, Mucahid Kutlu, Shammur Absar Chowdhury, and Firoj Alam. 2024. Nativqa: Multilingual culturally-aligned natural query for llms. arXiv preprint arXiv:2407.09823. [find](https://scholar.google.com/scholar?q=Md+Arid+Hasan%2C+Maram+Hasanain%2C+Fatema+Ahmad%2C+Sahinur+Rahman+Laskar%2C+Sunaya+Upadhyay%2C+Vrunda+N+Sukhadia%2C+Mucahid+Kutlu%2C+Shammur+Absar+Chowdhury%2C+and+Firoj+Alam%2E+2024%2E+Nativqa%3A+Multilingual+culturally%2Da)
30. Dieuwke Hupkes and Nikolay Bogoychev. 2025. [Multiloko: a multilingual local knowledge benchmark for llms spanning 31 languages](https://md.research.elamur.ai/free/en/papers/hupkes-2025-multiloko-multilingual-local-knowledge-benchmark/). arXiv preprint arXiv:2504.10356.
31. Kushal Jain, Adwait Deshpande, Kumar Shridhar, Felix Laumann, and Ayushman Dash. 2020. Indic-transformers: An analysis of transformer language models for indian languages. arXiv preprint arXiv:2011.02323. — [arXiv:2011.02323](https://arxiv.org/abs/2011.02323) [find](https://scholar.google.com/scholar?q=Kushal+Jain%2C+Adwait+Deshpande%2C+Kumar+Shridhar%2C+Felix+Laumann%2C+and+Ayushman+Dash%2E+2020%2E+Indic%2Dtransformers%3A+An+analysis+of+transformer+language+models+for+indian+languages%2E+arXiv+preprint+arXiv%3A2011%2E02)
32. Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul NC, 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 computational…
33. Elise Karinshak, Amanda Hu, Kewen Kong, Vish-wanatha Rao, Jingren Wang, Jindong Wang, and Yi Zeng. 2024. Llm-globe: A benchmark evaluating the cultural values embedded in llm output. arXiv preprint arXiv:2411.06032. — [arXiv:2411.06032](https://arxiv.org/abs/2411.06032) [find](https://scholar.google.com/scholar?q=Elise+Karinshak%2C+Amanda+Hu%2C+Kewen+Kong%2C+Vish%2Dwanatha+Rao%2C+Jingren+Wang%2C+Jindong+Wang%2C+and+Yi+Zeng%2E+2024%2E+Llm%2Dglobe%3A+A+benchmark+evaluating+the+cultural+values+embedded+in+llm+output%2E+arXiv+preprint+ar)
34. Shaharukh Khan, Ayush Tarun, Abhinav Ravi, Ali Faraz, Praveen Kumar Pokala, Anagha Bhangare, Raja Kolla, Chandra Khatri, and Shubham Agarwal. 2024. Chitrarth: Bridging vision and language for a billion people. In NeurIPS Multimodal Algorithmic Reasoning. [find](https://scholar.google.com/scholar?q=Shaharukh+Khan%2C+Ayush+Tarun%2C+Abhinav+Ravi%2C+Ali+Faraz%2C+Praveen+Kumar+Pokala%2C+Anagha+Bhangare%2C+Raja+Kolla%2C+Chandra+Khatri%2C+and+Shubham+Agarwal%2E+2024%2E+Chitrarth%3A+Bridging+vision+and+language+for+a+billio)
35. Khyati Khandelwal, Manuel Tonneau, Andrew M. Bean, Hannah Rose Kirk, and Scott A. Hale. 2024. Indian-bhed: A dataset for measuring india-centric biases in large language models. In Proceedings of the 2024 International Conference on Information Technology for Social Good, GoodIT ’24, page 231–239, N… [find](https://scholar.google.com/scholar?q=Khyati+Khandelwal%2C+Manuel+Tonneau%2C+Andrew+M%2E+Bean%2C+Hannah+Rose+Kirk%2C+and+Scott+A%2E+Hale%2E+2024%2E+Indian%2Dbhed%3A+A+dataset+for+measuring+india%2Dcentric+biases+in+large+language+models%2E+In+Proceedings+of+the+)
36. Julia Kharchenko, Tanya Roosta, Aman Chadha, and Chirag Shah. 2024. How well do llms represent values across cultures? empirical analysis of llm responses based on hofstede cultural dimensions. arXiv preprint arXiv:2406.14805. — [arXiv:2406.14805](https://arxiv.org/abs/2406.14805) [find](https://scholar.google.com/scholar?q=Julia+Kharchenko%2C+Tanya+Roosta%2C+Aman+Chadha%2C+and+Chirag+Shah%2E+2024%2E+How+well+do+llms+represent+values+across+cultures%3F+empirical+analysis+of+llm+responses+based+on+hofstede+cultural+dimensions%2E+arXiv+)
37. Fajri Koto, Rahmad Mahendra, Nurul Aisyah, and Timothy Baldwin. 2024. [Indoculture: Exploring geographically influenced cultural commonsense reasoning across eleven indonesian provinces](https://md.research.elamur.ai/free/en/papers/koto-2024-indoculture-exploring-geographically-influenced-cultural/). Transactions of the Association for Computational Linguistics, 12:1703–1719.
38. Raghvendra Kumar, Mohammed Salman S A, Aryan Sahu, Tridib Nandi, Pragathi Y P, Sriparna Saha, and Jose G Moreno. 2025. COSMMIC: Comment-sensitive multimodal multilingual Indian corpus for summarization and headline generation. In Proceedings of the 63rd Annual Meeting of the Association for Computat… [find](https://scholar.google.com/scholar?q=Raghvendra+Kumar%2C+Mohammed+Salman+S+A%2C+Aryan+Sahu%2C+Tridib+Nandi%2C+Pragathi+Y+P%2C+Sriparna+Saha%2C+and+Jose+G+Moreno%2E+2025%2E+COSMMIC%3A+Comment%2Dsensitive+multimodal+multilingual+Indian+corpus+for+summarizatio)
39. Raghvendra Kumar, Ratul Chakraborty, Abhishek Tiwari, Sriparna Saha, and Naveen Saini. 2023. Diving into a sea of opinions: Multi-modal abstractive summarization with comment sensitivity. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 1117–1126… [find](https://scholar.google.com/scholar?q=Raghvendra+Kumar%2C+Ratul+Chakraborty%2C+Abhishek+Tiwari%2C+Sriparna+Saha%2C+and+Naveen+Saini%2E+2023%2E+Diving+into+a+sea+of+opinions%3A+Multi%2Dmodal+abstractive+summarization+with+comment+sensitivity%2E+In+Proceedin)
40. Raghvendra Kumar, Deepak Prakash, Sriparna Saha, and Shubham Sharma. 2024. Indicbart alongside visual element: multimodal summarization in diverse indian languages. In International Conference on Document Analysis and Recognition, pages 264–280. Springer. [find](https://scholar.google.com/scholar?q=Raghvendra+Kumar%2C+Deepak+Prakash%2C+Sriparna+Saha%2C+and+Shubham+Sharma%2E+2024%2E+Indicbart+alongside+visual+element%3A+multimodal+summarization+in+diverse+indian+languages%2E+In+International+Conference+on+Docu)
41. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, and 1 others. 2022. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110. [find](https://scholar.google.com/scholar?q=Percy+Liang%2C+Rishi+Bommasani%2C+Tony+Lee%2C+Dimitris+Tsipras%2C+Dilara+Soylu%2C+Michihiro+Yasunaga%2C+Yian+Zhang%2C+Deepak+Narayanan%2C+Yuhuai+Wu%2C+Ananya+Kumar%2C+and+1+others%2E+2022%2E+Holistic+evaluation+of+language+m)
42. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2023. [Improved baselines with visual instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/). Preprint, arXiv:2310.03744.
43. Jabez Magomere, Shu Ishida, Tejumade Afonja, Aya Salama, Daniel Kochin, Foutse Yuehgoh, Imane Hamzaoui, Raesetje Sefala, Aisha Alaagib, Samantha Dalal, Beatrice Marchegiani, Elizaveta Semenova, Lauren Crais, and Siobhan Mackenzie Hall. 2025. The world wide recipe: A community-centred framework for f… — [arXiv:2406.09496](https://arxiv.org/abs/2406.09496) [find](https://scholar.google.com/scholar?q=Jabez+Magomere%2C+Shu+Ishida%2C+Tejumade+Afonja%2C+Aya+Salama%2C+Daniel+Kochin%2C+Foutse+Yuehgoh%2C+Imane+Hamzaoui%2C+Raesetje+Sefala%2C+Aisha+Alaagib%2C+Samantha+Dalal%2C+Beatrice+Marchegiani%2C+Elizaveta+Semenova%2C+Lauren)
44. Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora, Junho Myung, Srishti Yadav, Faiz Ghifari Haznitrama, Inhwa Song, Alice Oh, and Isabelle Augenstein. 2024. [Survey of cultural awareness in language models: Text and beyond](https://md.research.elamur.ai/free/en/papers/pawar-2025-survey-cultural-awareness-language-models/). Preprint, arXiv:2411.00860.
45. 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. arXiv 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)
46. Arijit Maji, Raghvendra Kumar, Akash Ghosh, Anushka Anushka, and Sriparna Saha. 2025. SANSKRITI: A comprehensive benchmark for evaluating language models’ knowledge of Indian culture. In Findings of the Association for Computational Linguistics: ACL 2025, pages 4434–4451, Vienna, Austria. Associatio… [find](https://scholar.google.com/scholar?q=Arijit+Maji%2C+Raghvendra+Kumar%2C+Akash+Ghosh%2C+Anushka+Anushka%2C+and+Sriparna+Saha%2E+2025%2E+SANSKRITI%3A+A+comprehensive+benchmark+for+evaluating+language+models%E2%80%99+knowledge+of+Indian+culture%2E+In+Findings+of+t)
47. David Romero, Chenyang Lyu, Haryo Wibowo, Santiago Góngora, Aishik Mandal, Sukannya Purkayastha, Jesus-German Ortiz-Barajas, Emilio Cueva, Jinheon Baek, Soyeong Jeong, and 1 others. 2025. Cvqa: Culturally-diverse multilingual visual question answering benchmark. Advances in Neural Information Proces… [find](https://scholar.google.com/scholar?q=David+Romero%2C+Chenyang+Lyu%2C+Haryo+Wibowo%2C+Santiago+G%C3%B3ngora%2C+Aishik+Mandal%2C+Sukannya+Purkayastha%2C+Jesus%2DGerman+Ortiz%2DBarajas%2C+Emilio+Cueva%2C+Jinheon+Baek%2C+Soyeong+Jeong%2C+and+1+others%2E+2025%2E+Cvqa%3A+Cultur)
48. Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro von Werra, and Thomas Wolf. 2025. Smolvlm: Redefining small and ef… [find](https://scholar.google.com/scholar?q=Andr%C3%A9s+Marafioti%2C+Orr+Zohar%2C+Miquel+Farr%C3%A9%2C+Merve+Noyan%2C+Elie+Bakouch%2C+Pedro+Cuenca%2C+Cyril+Zakka%2C+Loubna+Ben+Allal%2C+Anton+Lozhkov%2C+Nouamane+Tazi%2C+Vaibhav+Srivastav%2C+Joshua+Lochner%2C+Hugo+Larcher%2C+Mathie)
49. Jonathan Rystrøm, Hannah Rose Kirk, and Scott Hale. 2025. Multilingual!= multicultural: Evaluating gaps between multilingual capabilities and cultural alignment in llms. arXiv preprint arXiv:2502.16534. [find](https://scholar.google.com/scholar?q=Jonathan+Rystr%C3%B8m%2C+Hannah+Rose+Kirk%2C+and+Scott+Hale%2E+2025%2E+Multilingual%21%3D+multicultural%3A+Evaluating+gaps+between+multilingual+capabilities+and+cultural+alignment+in+llms%2E+arXiv+preprint+arXiv%3A2502%2E1653)
50. Junho Myung, Nayeon Lee, Yi Zhou, Jiho Jin, Rifki Putri, Dimosthenis Antypas, Hsuvas Borkakoty, Eunsu Kim, Carla Perez-Almendros, Abinew Ali Ayele, and 1 others. 2024. [Blend: A benchmark for llms on everyday knowledge in diverse cultures and languages](https://md.research.elamur.ai/free/en/papers/myung-2024-blend-benchmark-llms-everyday-knowledge/). Advances in Neural Information Processing Syste…
51. Shravan Nayak, Kanishk Jain, Rabiul Awal, Siva Reddy, Sjoerd Van Steenkiste, Lisa Anne Hendricks, Karolina Stanczak, and Aishwarya Agrawal. 2024. [Benchmarking vision language models for cultural understanding](https://md.research.elamur.ai/free/en/papers/nayak-2024-benchmarking-vision-language-models-cultural/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing,…
52. Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Kazuki Egashira, Jeonghun Baek, Xiang Yue, Graham Neubig, and Kiyoharu Aizawa. 2024. Jmmmu: A japanese massive multi-discipline multimodal understanding benchmark for culture-aware evaluation. arXiv preprint arXiv:2410.17250. [find](https://scholar.google.com/scholar?q=Shota+Onohara%2C+Atsuyuki+Miyai%2C+Yuki+Imajuku%2C+Kazuki+Egashira%2C+Jeonghun+Baek%2C+Xiang+Yue%2C+Graham+Neubig%2C+and+Kiyoharu+Aizawa%2E+2024%2E+Jmmmu%3A+A+japanese+massive+multi%2Ddiscipline+multimodal+understanding+be)
53. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. [Train…](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
54. Nihar Sahoo, Pranamya Kulkarni, Arif Ahmad, Tanu Goyal, Narjis Asad, Aparna Garimella, and Pushpak Bhattacharyya. 2024a. 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 Associatio… [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+2024a%2E+Indibias%3A+A+benchmark+dataset+to+measure+social+biases+in+language+models+for+i)
55. Pranab Sahoo, Prabhash Meharia, Akash Ghosh, Sriparna Saha, Vinija Jain, and Aman Chadha. 2024b. Unveiling hallucination in text, image, video, and audio foundation models: A comprehensive review. [find](https://scholar.google.com/scholar?q=Pranab+Sahoo%2C+Prabhash+Meharia%2C+Akash+Ghosh%2C+Sriparna+Saha%2C+Vinija+Jain%2C+and+Aman+Chadha%2E+2024b%2E+Unveiling+hallucination+in+text%2C+image%2C+video%2C+and+audio+foundation+models%3A+A+comprehensive+review%2E)
56. Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024c. [A systematic survey of prompt engineering in large language models: Techniques and applications](https://md.research.elamur.ai/free/en/papers/sahoo-2024-systematic-survey-prompt-engineering-large/). arXiv preprint arXiv:2402.07927.
57. Florian Schneider, Carolin Holtermann, Chris Biemann, and Anne Lauscher. 2025. Gimmick–globally inclusive multimodal multitask cultural knowledge benchmarking. arXiv preprint arXiv:2502.13766. [find](https://scholar.google.com/scholar?q=Florian+Schneider%2C+Carolin+Holtermann%2C+Chris+Biemann%2C+and+Anne+Lauscher%2E+2025%2E+Gimmick%E2%80%93globally+inclusive+multimodal+multitask+cultural+knowledge+benchmarking%2E+arXiv+preprint+arXiv%3A2502%2E13766%2E)
58. Florian Schneider and Sunayana Sitaram. 2024. M5– a diverse benchmark to assess the performance of large multimodal models across multilingual and multicultural vision-language tasks. arXiv preprint arXiv:2407.03791. [find](https://scholar.google.com/scholar?q=Florian+Schneider+and+Sunayana+Sitaram%2E+2024%2E+M5%E2%80%93+a+diverse+benchmark+to+assess+the+performance+of+large+multimodal+models+across+multilingual+and+multicultural+vision%2Dlanguage+tasks%2E+arXiv+preprint+a)
59. Nithish Kannen Senthilkumar, Arif Ahmad, Marco Andreetto, Vinodkumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, and Shachi Dave. 2024. Beyond aesthetics: Cultural competence in text-to-image models. Advances in Neural Information Processing Systems, 37:13716–13747. [find](https://scholar.google.com/scholar?q=Nithish+Kannen+Senthilkumar%2C+Arif+Ahmad%2C+Marco+Andreetto%2C+Vinodkumar+Prabhakaran%2C+Utsav+Prabhu%2C+Adji+Bousso+Dieng%2C+Pushpak+Bhattacharyya%2C+and+Shachi+Dave%2E+2024%2E+Beyond+aesthetics%3A+Cultural+competence+)
60. 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…
61. 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)
62. Shivalika Singh, Angelika Romanou, Clémentine Fourrier, David I Adelani, Jian Gang Ngui, Daniel Vila-Suero, Peerat Limkonchotiwat, Kelly Marchisio, Wei Qi Leong, Yosephine Susanto, and 1 others. 2024. [Global mmlu: Understanding and addressing cultural and linguistic biases in multilingual evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/)…
63. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, and 1 others. 2025a. Gemma 3 technical report. arXiv preprint arXiv:2503.19786. [find](https://scholar.google.com/scholar?q=Gemma+Team%2C+Aishwarya+Kamath%2C+Johan+Ferret%2C+Shreya+Pathak%2C+Nino+Vieillard%2C+Ramona+Merhej%2C+Sarah+Perrin%2C+Tatiana+Matejovicova%2C+Alexandre+Ram%C3%A9%2C+Morgane+Rivi%C3%A8re%2C+and+1+others%2E+2025a%2E+Gemma+3+technical+re)
64. Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, Congcong Wang, Dehao Zhang, Dikang Du, Dongliang Wang, Enming Yuan, Enzhe Lu, Fang Li, Flood Sung, Guangda Wei, and 73 others. 2025b. [Kimi-VL technical report](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-vl-technical-report/). Preprint, arXiv:2504.…
65. Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana, Noor Ahsan, Nevasini Sasikumar, Omkar Thawakar, Henok Biadglign Ademtew, Yahya Hmaiti, Amandeep Kumar, Kartik Kuckreja, and 1 others. 2024. All languages matter: Evaluating lmms on culturally diverse 100 languages. arXiv preprint arXiv:2411.16508… — [arXiv:2411.16508](https://arxiv.org/abs/2411.16508) [find](https://scholar.google.com/scholar?q=Ashmal+Vayani%2C+Dinura+Dissanayake%2C+Hasindri+Watawana%2C+Noor+Ahsan%2C+Nevasini+Sasikumar%2C+Omkar+Thawakar%2C+Henok+Biadglign+Ademtew%2C+Yahya+Hmaiti%2C+Amandeep+Kumar%2C+Kartik+Kuckreja%2C+and+1+others%2E+2024%2E+All+la)
66. Yash Verma, Anubhav Jangra, Raghvendra Verma, and Sriparna Saha. 2023. Large scale multi-lingual multimodal summarization dataset. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3620– 3632, Dubrovnik, Croatia. Association for Com… [find](https://scholar.google.com/scholar?q=Yash+Verma%2C+Anubhav+Jangra%2C+Raghvendra+Verma%2C+and+Sriparna+Saha%2E+2023%2E+Large+scale+multi%2Dlingual+multimodal+summarization+dataset%2E+In+Proceedings+of+the+17th+Conference+of+the+European+Chapter+of+the+)
67. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. 2024b. [Qwen2-vl: Enhancing vision-language model’s perception of the…](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/)
68. Weiyun Wang, Zhe Chen, Wenhai Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Jinguo Zhu, Xizhou Zhu, Lewei Lu, Yu Qiao, and Jifeng Dai. 2024c. [Enhancing the reasoning ability of multimodal large language models via mixed preference optimization](https://md.research.elamur.ai/free/en/papers/wang-2024-enhancing-reasoning-ability-multimodal-large/). arXiv preprint arXiv:2411.10442.
69. Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan, David Anugraha, Rifki Afina Putri, Wang Yutong, Adam Nohejl, Ubaidillah Ariq Prathama, Nedjma Ousidhoum, Afifa Amriani, and 1 others. 2025. [Worldcuisines: A massive-scale benchmark for multilingual and multicultural visual question answeri…](https://md.research.elamur.ai/free/en/papers/winata-2025-worldcuisines-massive-scale-benchmark-multilingual/)
70. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, and 1 others. 2025. Qwen2. 5-omni technical report. arXiv preprint arXiv:2503.20215. — [Qwen2.5-Omni Technical Report](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen2-5-omni-technical/)
71. Srishti Yadav, Zhi Zhang, Daniel Hershcovich, and Ekaterina Shutova. 2025. Beyond words: Exploring cultural value sensitivity in multimodal models. arXiv preprint arXiv:2502.14906. [find](https://scholar.google.com/scholar?q=Srishti+Yadav%2C+Zhi+Zhang%2C+Daniel+Hershcovich%2C+and+Ekaterina+Shutova%2E+2025%2E+Beyond+words%3A+Exploring+cultural+value+sensitivity+in+multimodal+models%2E+arXiv+preprint+arXiv%3A2502%2E14906%2E)

*Updated 31 October 2026*