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20242026
most citedBeyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning

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cs.CL2026

Jais 2: A Family of Arabic-Centric Open Large Language Models

Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim +57

Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong p…

cs.CL2026

The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs

Yang Zhang, Xiao Fei, Amr Mohamed +6

Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural knowledge is better accessed thr…

cs.CL2026

GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek

Yang Zhang, Mersin Konomi, Christos Xypolopoulos +6

Large Language Models (LLMs) are commonly trained on multilingual corpora that include Greek, yet reliable evaluation benchmarks for Greek-particularly those based on authentic, na…

cs.CL2026

Markovian Generation Chains in Large Language Models

Mingmeng Geng, Amr Mohamed, Guokan Shang +2

The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this itera…

cs.CL2025

Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules

Amr Mohamed, Yang Zhang, Michalis Vazirgiannis +1

Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We pre…

cs.CL2025

Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts

Guokan Shang, Hadi Abdine, Ahmad Chamma +7

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…

cs.CL2025

Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text

Amr Mohamed, Yang Zhang, Michalis Vazirgiannis +1

Code-switching (CSW) is the act of alternating between two or more languages within a single discourse. This phenomenon is widespread in multilingual communities, and increasingly…

cs.CL2025★ 1 cited

Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning

Yang Zhang, Amr Mohamed, Hadi Abdine +2

Curriculum learning-organizing training data from easy to hard-has improved efficiency across machine learning domains, yet remains underexplored for language model pretraining. We…

cs.CL2025

LLM as a Broken Telephone: Iterative Generation Distorts Information

Amr Mohamed, Mingmeng Geng, Michalis Vazirgiannis +1

As large language models are increasingly responsible for online content, concerns arise about the impact of repeatedly processing their own outputs. Inspired by the "broken teleph…

cs.CL2024

Benchmarking Linguistic Diversity of Large Language Models

Yanzhu Guo, Guokan Shang, Chloé Clavel

The development and evaluation of Large Language Models (LLMs) has primarily focused on their task-solving capabilities, with recent models even surpassing human performance in som…