157 citations · 276 across the 5 of their papers we have counts for
5 papers
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…
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…
Falcon2-11B Technical Report
Quentin Malartic, Nilabhra Roy Chowdhury, Ruxandra Cojocaru +14
We introduce Falcon2-11B, a foundation model trained on over five trillion tokens, and its multimodal counterpart, Falcon2-11B-vlm, which is a vision-to-text model. We report our f…
The Falcon Series of Open Language Models
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi +11
We introduce the Falcon series: 7B, 40B, and 180B parameters causal decoder-only models trained on a diverse high-quality corpora predominantly assembled from web data. The largest…
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only
Guilherme Penedo, Quentin Malartic, Daniel Hesslow +6
Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This…