3 citations · 5 across the 23 of their papers we have counts for
6 papers · 1 filter
SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models
Sofian Chaybouti, Sanath Narayan, Yasser Dahou +6
Vision foundation models trained via multi-teacher distillation offer a promising path toward unified visual representations, yet the learning dynamics and data efficiency of such…
Evaluating Arabic Large Language Models: A Survey of Benchmarks, Methods, and Gaps
Ahmed Alzubaidi, Shaikha Alsuwaidi, Basma El Amel Boussaha +5
This survey provides the first systematic review of Arabic LLM benchmarks, analyzing 40+ evaluation benchmarks across NLP tasks, knowledge domains, cultural understanding, and spec…
Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Gokul Karthik Kumar, Rishabh Saraf, Ludovick Lepauloux +3
Large language models (LLMs) have transformed NLP, yet their integration with audio remains underexplored despite audio's centrality to human communication. We introduce Falcon3-Au…
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed +24
In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across…
3LM: Bridging Arabic, STEM, and Code through Benchmarking
Basma El Amel Boussaha, Leen AlQadi, Mugariya Farooq +5
Arabic is one of the most widely spoken languages in the world, yet efforts to develop and evaluate Large Language Models (LLMs) for Arabic remain relatively limited. Most existing…
NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models
Mouadh Yagoubi, Yasser Dahou, Billel Mokeddem +12
Existing benchmarks have proven effective for assessing the performance of fully trained large language models. However, we find striking differences in the early training stages o…