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.

Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4%), match the best results for Classical Arabic (5.4%), and report competitive results for dialectal Arabic.