1 citations · 1 across the 10 of their papers we have counts for
15 papers
Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech
Yentl Collin, Evan Dufraisse, Amr Mohamed +3
Flow-matching text-to-speech (TTS) models achieve high synthesis quality but require many neural function evaluations (NFEs) to integrate their generative trajectories. Recent few-…
CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models
Wail Bouhedja, Amr Mohamed, Guokan Shang
Masked diffusion language models predict tokens from a partially observed response canvas, enabling bidirectional conditioning and parallel token refinement. Yet standard masked-di…
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…
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…
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…
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…
YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation
Abdelaziz Bounhar, Rania Hossam Elmohamady Elbadry, Hadi Abdine +3
Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine-tuning for alignment and personalization. Recent work on Bi-…
MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models
Ahmad Chamma, Omar El Herraoui, Guokan Shang
We introduce MixtureKit, a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned models.…
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…
Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR
Abdelaziz Bounhar, Hadi Abdine, Evan Dufraisse +5
Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (R…
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…
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…
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…
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…
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…