Aladdin-FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation
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Abstract (arXiv)
Arabic dialects have long been under-represented in Natural Language Processing (NLP) research due to their non-standardization and high variability, which pose challenges for computational modeling. Recent advances in the field, such as Large Language Models (LLMs), offer promising avenues to address this gap by enabling Arabic to be modeled as a pluricentric language rather than a monolithic system. This paper presents Aladdin-FTI, our submission to the AMIYA shared task. The proposed system is designed to both generate and translate dialectal Arabic (DA). Specifically, the model supports text generation in Moroccan, Egyptian, Palestinian, Syrian, and Saudi dialects, as well as bidirectional translation between these dialects, Modern Standard Arabic (MSA), and English. The code and trained model are publicly available.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 45 references that are in the map. First posted on arXiv 18 Feb 2026.
Builds on
12 of 45 references.
References · 12
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- 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
- 2023Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedbackcited by 189
- 2024Understanding and Mitigating Language Confusion in LLMscited by 75
- 2018Unsupervised Statistical Machine Translationcited by 51
- 2022A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methodscited by 42
- 2022Curras + Baladi: Towards a Levantine Corpuscited by 20
- 2024AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabiccited by 11
- 2024The Evolution of Darija Open Dataset: Introducing Version 2cited by 3
- 2025Saudi-Dialect-ALLaM: LoRA Fine-Tuning for Dialectal Arabic Generationcited by 2
- 2025Arabizi vs LLMs: Can the Genie Understand the Language of Aladdin?cited by 1
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Citing papers per year: 2026: 1.
1 paper.