Papers with cultural sensitivity
Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have shown growing potential in offering emotional support, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. |
| Approach: | They propose a large language model dataset that includes 1,729 distress messages, 1,523 cultural signals and 1,041 support strategies with fine-grained emotional and cultural annotations. |
| Outcome: | The proposed models outperform peer-reviewed models and lack cultural sensitivity. |
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)
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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf
| Challenge: | Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. |
| Approach: | They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability. |
| Outcome: | The proposed system can understand and remediate norm violations step by step. |
EtiCor++: Towards Understanding Etiquettical Bias in LLMs (2025.findings-acl)
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| Challenge: | Etiquettes are region-specific and are an essential part of the culture of a region. |
| Approach: | They introduce EtiCor++, a corpus of etiquettes worldwide, to evaluate LLMs for their knowledge about etiques across regions. |
| Outcome: | The proposed corpus of etiquettes shows that LLMs are biased towards certain regions. |
Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore’s Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have transformed natural language processing, but their safety mechanisms remain under-explored in low-resource, multilingual settings. |
| Approach: | They propose a red-teaming approach to probe LLM vulnerabilities in Singapore's diverse linguistic context using a dataset and evaluation framework. |
| Outcome: | The proposed framework systematically probes LLM vulnerabilities in three real-world scenarios including Singlish, Chinese, Malay, and Tamil. |
Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition (2025.findings-acl)
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| Challenge: | Existing research has evaluated large language models' cultural knowledge and contextual understanding, reducing their effectiveness in multicultural settings. |
| Approach: | They propose a benchmark to evaluate LLMs' cultural understanding with a focus on Korean superstitions. |
| Outcome: | The proposed benchmark assesses multilingual LLMs in Korean and English to analyze their ability to reason about Korean cultural contexts and how language variations affect performance. |
MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages (2026.findings-acl)
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| Challenge: | Existing evaluation datasets lack cross-lingual alignment, leaving assessments of multilingual capabilities fragmented in both language and skill coverage. |
| Approach: | They propose to use multilingual consistency as a complementary metric to assess performance bottlenecks and guide model improvement. |
| Outcome: | The proposed model lacks cross-lingual alignment and language coverage gaps between state-of-the-art models. |
Fann or Flop: A Multigenre, Multiera Benchmark for Arabic Poetry Understanding in LLMs (2025.emnlp-main)
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Wafa Al Ghallabi, Ritesh Thawkar, Sara Ghaboura, Ketan Pravin More, Omkar Thawakar, Hisham Cholakkal, Salman Khan, Rao Muhammad Anwer
| Challenge: | a benchmark is designed to assess the comprehension of Arabic poetry by large language models in 12 historical eras. |
| Approach: | They propose a benchmark to assess the comprehension of Arabic poetry by large language models in 12 historical eras. |
| Outcome: | The benchmark assesses the comprehension of Arabic poetry by large language models in 12 historical eras. |
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users (2025.acl-long)
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Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos, Danae Sanchez Villegas, Phillip Rust, Ruchira Dhar, Daniel Hershcovich, Anders Søgaard
| Challenge: | Despite high adoption rate of Large Language Models, there are limitations related to contextual understanding, cultural sensitivity, and complex scene understanding. |
| Approach: | They conduct a user survey to identify adoption patterns and key challenges users face with such technologies. |
| Outcome: | The proposed models have high adoption rates but still face limitations in visual aids. |
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs (2025.acl-long)
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Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, AbdelRahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, Ahmed Oumar El-Shangiti, Aisha Alraeesi, Mohammed Anwar AL-Ghrawi, Abdulrahman S. Al-Batati, Elgizouli Mohamed, Noha Taha Elgindi, Muhammed Saeed, Houdaifa Atou, Issam Ait Yahia, Abdelhak Bouayad, Mohammed Machrouh, Amal Makouar, Dania Alkawi, Mukhtar Mohamed, Safaa Taher Abdelfadil, Amine Ziad Ounnoughene, Anfel Rouabhia, Rwaa Assi, Ahmed Sorkatti, Mohamedou Cheikh Tourad, Anis Koubaa, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | a year-long community-driven project covering all 22 Arab countries evaluates the cultural and dialectal capabilities of large language models. |
| Approach: | They propose a project to evaluate the cultural and dialectal capabilities of large language models. |
| Outcome: | The project evaluates the cultural and dialectal capabilities of several frontier LLMs. |