Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
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.
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Guokan Shang
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Guokan Shang
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Hadi Abdine
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Hadi Abdine
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Ahmad Chamma
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Ahmad Chamma
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Amr Mohamed
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Amr Mohamed
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Mohamed Anwar
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Mohamed Anwar
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Abdelaziz Bounhar
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Abdelaziz Bounhar
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Omar El Herraoui
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Preslav Nakov
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Preslav Nakov
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Michalis Vazirgiannis
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Michalis Vazirgiannis
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Eric Xing
Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
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.
Nile-Chat models, using a Branch-Train-MiX strategy, outperform existing multilingual and Arabic LLMs on Egyptian dialect benchmarks, particularly in dual-script understanding and generation.
LLMs Egyptian dialect Arabic Latin scripts Branch-Train-MiX MoE model multilingual Jais ALLaM Egyptian evaluation benchmarks Qwen2.5-14B-Instruct