Model Card for unige-fti/Aladdin-3B
Multidialectal Arabic generation and translation model fine-tuned for dialect fidelity and diglossia.
Model Details
Model Description
- Base model: SmolLM3-3B
- Architecture: Decoder-only causal transformer (SmolLM architecture)
- Parameters: ~3B
- Language coverage: Arabic dialects, Modern Standard Arabic (MSA), English
Primary tasks:
- Dialectal Arabic generation
- Bidirectional translation (DA ↔ MSA ↔ English)
- Controlled generation conditioned on dialect instructions
This model was fine-tuned by the Aladdin-FTI team for the AMIYA shared task to jointly optimize:
- Machine translation (semantic adequacy & diglossia)
Instruction-formatted prompts:
Translate from English into Egyptian Arabic:
<SOURCE>
- Instruction-conditioned generation (dialect fidelity)
Complete the sentence in Moroccan Arabic:
<PREFIX>
The objective balances meaning preservation and dialect naturalness in Arabic diglossia settings.
Model Sources
- Repository: Github repository
- Paper: https://arxiv.org/abs/2602.16290
How to Get Started with the Model
TODO
Training Details
Training Data: Closed-track training data only.
Datasets span multiple dialect regions and domains
Parallel corpora:
- SauDial
- Casablanca corpus
- JODA
- UFAL Levantine
- DODA
- Atlas
Monolingual dialect corpora:
- MADAR
- Shami
- Saudi Tweets
- EDGAD / EDC
- HABIBI lyrics
Citation
If you use this model in your research, please cite the following paper:
@inproceedings{mutal2026aladdinfti,
title = {Aladdin-FTI @ AMIYA: Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation},
author = {Mutal, Jonathan and Al Almaoui, Perla and Hengchen, Simon and Bouillon, Pierrette},
booktitle = {Proceedings of the AMIYA Shared Task, co-located with VarDial at EACL 2026},
year = {2026},
address = {Rabat, Morocco},
publisher = {Association for Computational Linguistics},
}
Compute infrastructure
The computations were performed at the University of Geneva using the Baobab HPC service.