JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking (2025.naacl-long)
Copied to clipboard
Samar Mohamed Magdy, Sang Yun Kwon, Fakhraddin Alwajih, Safaa Taher Abdelfadil, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | Recent advances in instruction fine-tuning and alignment methods have enhanced the adaptability of large language models to user preferences. |
| Approach: | They propose a benchmark to assess LLMs’ capacity to comprehend and interpret Arabic proverbs. |
| Outcome: | The proposed model can generate accurate translations, but struggle to produce culturally nuanced and contextually relevant explanations. |
Similar Papers
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs’ Cultural Processing of Figurative Language (2026.eacl-long)
Copied to clipboard
| Challenge: | Using figurative language as a proxy for cultural nuance and local knowledge, large language models struggle with connotative meaning. |
| Approach: | They evaluate large language models' ability to process culturally grounded language . they use figurative language as a proxy for cultural nuance and local knowledge . |
| Outcome: | The proposed models can understand and use figurative expressions that encode local knowledge and social nuance. |
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
Copied to clipboard
Basel Mousi, Nadir Durrani, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain, Tameem Kabbani, Fahim Dalvi, Shammur Absar Chowdhury, Firoj Alam
| Challenge: | a recent study has found that Arabic is underrepresented in Large Language Models, especially in dialectal variations. |
| Approach: | They propose a benchmark for Arabic Dialect and Cultural Evaluation that evaluates Arabic dialect comprehension and generation. |
| Outcome: | The proposed model outperforms multilingual models on dialect comprehension and generation, but significant challenges persist in dialect identification, generation, and translation. |
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)
Copied to clipboard
Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
| Challenge: | Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues. |
| Approach: | They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. |
| Outcome: | The proposed model performs worse on all three tasks than the MSA benchmark. |
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs (2025.acl-long)
Copied to clipboard
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. |
FFE-Hallu: Hallucinations in Fixed Figurative Expressions: A Benchmark of Idioms and Proverbs in the Persian Language (2026.eacl-long)
Copied to clipboard
| Challenge: | Figurative language, especially fixed figurative expressions, poses unique challenges for large language models . Unlike literal phrases, FFEs are culturally grounded and often non-compositional, making them vulnerable to figurativ hallucination . |
| Approach: | They propose a benchmark to evaluate LLMs' ability to generate, detect, and translate fixed figurative expressions in Persian. |
| Outcome: | The proposed benchmarks show that LLMs still struggle with figurative language expressions . the benchmarks are based on 600 carefully curated examples spanning three tasks . |
AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic (2025.findings-acl)
Copied to clipboard
| Challenge: | Dialectal Arabic (DA) varieties are under-served by language technologies, particularly large language models (LLMs). |
| Approach: | They propose a framework that comprehensively assesses LLMs’ DA modeling capabilities across four dimensions: fidelity, understanding, quality, and diglossia. |
| Outcome: | The proposed framework assesses LLMs’ DA modeling capabilities across four dimensions: fidelity, understanding, quality, and diglossia. |
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent research has demonstrated that large language models (LLMs) can translate cultural elements in languages such as idioms and proverbs. |
| Approach: | They propose to use large language models to translate culturally rooted proverbs in conversation and between languages with similar cultural backgrounds to compare their results. |
| Outcome: | The proposed models can achieve good translation between languages with similar cultural backgrounds and outperform NMT models in proverb translation. |
AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models have shown remarkable progress in reasoning abilities and general natural language processing tasks, yet their performance on Arabic data remains underexplored. |
| Approach: | They compare reasoning-focused LLMs with deepSeek models across 15 Arabic NLP tasks . they use zero-shot, few-shot and fine-tuning to evaluate their capacity for linguistic reasoning . |
| Outcome: | The proposed models outperform strong models on Arabic datasets and are compared with other models. |
Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are adept at question answering and reasoning tasks, but when reasoning in situational context, human expectations vary depending on the relevant cultural common ground. |
| Approach: | They construct and evaluate a dataset for proverb understanding with conversational context for six different languages and their usage within the context. |
| Outcome: | The proposed model is able to reason with proverbs and sayings in conversational contexts. |
TounsiBench: Benchmarking Large Language Models for Tunisian Arabic (2025.emnlp-main)
Copied to clipboard
| Challenge: | a dataset of Tunisian Arabic instructions and prompts is used to evaluate LLMs' ability to understand and generate responses in Tunisia . we assess the quality, correctness, relevance, and dialectal adherence of LLM responses . |
| Approach: | They propose a benchmark for evaluating the capabilities of large language models in Tunisian Arabic . they use a dataset of Tunisia Arabic instructions and prompts to evaluate their models . |
| Outcome: | The proposed model can judge quality, correctness, relevance, and dialectal adherence . the model can also generate a leaderboard for the Tunisian Arabic language . |