2 citations · 3 across the 8 of their papers we have counts for
12 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-…
The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis
Ahmed Asaad, Amr Mohamed, Yang Zhang +1
Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalization can affect evidence-based…
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…
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…
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…
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect
Guokan Shang, Hadi Abdine, Yousef Khoubrane +9
We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruct…