Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait, Saied Alshahrani, Gubran Abdulrahman, Gamil Ahmed, Qais Gawah, Zead Saleh, Mustafa Ghaleb, Yousef Ali, Maged Al-shaibani
| Challenge: | Instruction tuning datasets predominantly cater to English or are derived from English-dominated LLMs. |
| Approach: | They propose to use an Arabic instruction tuning dataset culturally aligned by native Arabic speakers to address drawbacks of finetuning LLMs on machine-generated or machinetranslated datasets. |
| Outcome: | The proposed datasets show that they achieve better cultural alignment than models fine-tuned on other datasets. |
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| Challenge: | Large language models exhibit cultural bias from over-represented viewpoints in training data, yet cultural alignment remains a challenge due to limited cultural knowledge and a lack of exploration into effective learning approaches. |
| Approach: | They propose a cost-efficient method for fine-tuning large language models on native speakers’ word-association norms and a preference optimization method to improve cultural alignment. |
| Outcome: | The proposed model trains Llama-3.1-8B and Qwen-2.5-7B on native speakers’ word-association norms and shows that such associations capture cultural knowledge. |
MAIN: Mutual Alignment Is Necessary for instruction tuning (2025.emnlp-main)
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Fanyi Yang, Jianfeng Liu, Xin Zhang, Haoyu Liu, Xixin Cao, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Instruction tuning has enabled large language models to achieve remarkable performance, yet its success heavily depends on the availability of high-quality instruction-response pairs. |
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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. |
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AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
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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. |
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Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh (2025.acl-long)
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| Challenge: | Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains. |
| Approach: | They propose to open-source a large-scale instruction-following dataset covering key institutional and cultural knowledge relevant to Kazakhstan. |
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NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)
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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King
| Challenge: | Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance. |
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Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
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| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
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CultureInstruct: Curating Multi-Cultural Instructions at Scale (2025.naacl-long)
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| Challenge: | Large language models exhibit severe cultural bias, despite their success in recent years . a critical challenge of LLMs is integration of cultural knowledge into these models . |
| Approach: | They propose a large-scale instruction-tuning dataset to reduce cultural bias in large language models. |
| Outcome: | The proposed model outperforms GPT-4o Mini and GPT-42 with 18.47% and 13.07% relative improvements on cultural benchmarks. |
RECOST: External Knowledge Guided Data-efficient Instruction Tuning (2024.findings-acl)
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| Challenge: | Considering the high computing power overhead, data-efficient instruction tuning is proposed to reduce the training data size. |
| Approach: | They propose a framework to improve instruction tuning by integrating external knowledge into a single pipeline. |
| Outcome: | The proposed method achieves better results with only 1% of the full dataset. |
PROM: Pivoted and Regulated Optimization for Multilingual Instruction Learning (2025.naacl-short)
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| Challenge: | Existing solutions to large language models (LLMs) are English-centric, hindering their application to 6500+ existing languages. |
| Approach: | They propose to append English tuning data with its translated pair to solve this problem . they identify English as an internal pivot language and propose to regulate between them . |
| Outcome: | The proposed model is able to generalize on multiple benchmarks across different languages. |