Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
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Abstract (arXiv)
We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for Egyptian dialect, uniquely designed to understand and generate texts written in both Arabic and Latin scripts. Specifically, with Nile-Chat-3x4B-A6B, we introduce a novel language adaptation approach by leveraging the Branch-Train-MiX strategy to merge script-specialized experts, into a single MoE model. Our Nile-Chat models significantly outperform leading multilingual and Arabic LLMs, such as LLaMa, Jais, and ALLaM, on our newly introduced Egyptian evaluation benchmarks, which span both understanding and generative tasks. Notably, our 12B model yields a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks. All our resources are publicly available. We believe this work presents a comprehensive methodology for adapting LLMs to dual-script languages, addressing an often overlooked aspect in modern LLM development.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 57 references that are in the map. First posted on arXiv 6 Jul 2025.
Builds on
31 of 57 references.
References · 31
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19 more
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Built on by
Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2026GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greekcited by 6
- 2026From FusHa to Folk: Exploring Cross-Lingual Transfer in Arabic Language Modelscited by 1
- 2026Linear Semantic Segmentation for Low-Resource Spoken Dialectscited by 1
- 2025Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translationcited by 0
- 2025MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Modelscited by 0
- 2026The Hrunting of AI: Where and How to Improve English Dialectal Fairnesscited by 0
- 2026Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMscited by 0
- 2026Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMs21 Sep 2026
- 2026Linear Semantic Segmentation for Low-Resource Spoken Dialects7 May 2026
- 2026The Hrunting of AI: Where and How to Improve English Dialectal Fairness16 Mar 2026
- 2026From FusHa to Folk: Exploring Cross-Lingual Transfer in Arabic Language Models10 Feb 2026
- 2026GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek5 Feb 2026
- 2025MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models13 Dec 2025
- 2025Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation21 Nov 2025
Similar, not cited
Close in wording, no citation either way
- 2025NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities
- 2026LLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models
- 2023Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
- 2025UI-Level Evaluation of ALLaM 34B: Measuring an Arabic-Centric LLM via HUMAIN Chat
- 2025Command R7B Arabic: A Small, Enterprise Focused, Multilingual, and Culturally Aware Arabic LLM
- 2025Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
- 2026RightNow-Arabic-0.5B-Turbo: An Open Sub-1B Arabic Language Model via Vocabulary Injection and Edge-First Deployment
- 2025DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models