BALSAM: A Platform for Benchmarking Arabic Large Language Models

The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it challenging to measure actual progress and to mitigate data contamination. Here, we aim to bridge these gaps. In particular, we introduce BALSAM, a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation. It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation. We envision BALSAM as a unifying platform that sets standards and promotes collaborative research to advance Arabic LLM capabilities.

Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification

Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with variability of patterns caused by factors such as writer age. Most of the studies focused on adults, with only one recent study on children. Moreover, much of the recent Machine Learning methods focused on using Convolutional Neural Networks, a powerful class of neural networks that can extract complex features from images. In this paper we propose a convolutional neural network (CNN) model that recognizes children handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of the Arabic characters written by children, and 97% on Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it reveals a bigger challenge to solve for children Arabic handwritten character recognition. Moreover, we proposed a new approach using multi models instead of single model based on the number of strokes in a character, and merged Hijja with AHCD which reached an averaged prediction accuracy of 96%.