3 citations · 5 across the 23 of their papers we have counts for
6 papers · 1 filter
Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training
Jingwei Zuo, Cong Zeng, Ilyas Chahed +6
The training paradigm of large language models has shifted from traditional one-pass training to multi-epoch training, as reasonable reuse of limited high-quality data can improve…
Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM Evaluation
Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai +6
We present QIMMA, a quality-assured Arabic LLM leaderboard that places systematic benchmark validation at its core. Rather than aggregating existing resources as-is, QIMMA applies…
ALRM: Agentic LLM for Robotic Manipulation
Vitor Gaboardi dos Santos, Ibrahim Khadraoui, Ibrahim Farhat +3
Large Language Models (LLMs) have recently empowered agentic frameworks to exhibit advanced reasoning and planning capabilities. However, their integration in robotic control pipel…
WavLink: Compact Audio-Text Embeddings with a Global Whisper Token
Gokul Karthik Kumar, Ludovick Lepauloux, Hakim Hacid
Whisper has become the de-facto encoder for extracting general-purpose audio features in large audio-language models, where a 30-second clip is typically represented by 1500 frame…
Learnable Multipliers: Freeing the Scale of Language Model Matrix Layers
Maksim Velikanov, Ilyas Chahed, Jingwei Zuo +3
Applying weight decay (WD) to matrix layers is standard practice in large-language-model pretraining. Prior work suggests that stochastic gradient noise induces a Brownian-like exp…
Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling
Falcon LLM Team, Iheb Chaabane, Puneesh Khanna +8
This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models…