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…
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…
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
Aymane El Firdoussi, Mohamed El Amine Seddik, Soufiane Hayou +3
Synthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e.g., Shumailov et al. (2023); Seddik et al. (2024)). A pot…
Alignment with Preference Optimization Is All You Need for LLM Safety
Reda Alami, Ali Khalifa Almansoori, Ahmed Alzubaidi +3
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achi…