The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for the Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%.

In this paper, we describe our submission to the WMT-2022: Large-Scale Machine Translation Evaluation for African Languages under the Constrained Translation track. We introduce DENTRA, a novel pre-training strategy for a multilingual sequence-to-sequence transformer model. DENTRA pre-training combines denoising and translation objectives to incorporate both monolingual and bitext corpora in 24 African, English, and French languages. To evaluate the quality of DENTRA, we fine-tuned it with two multilingual machine translation configurations, one-to-many and many-to-one. In both pre-training and fine-tuning, we employ only the datasets provided by the organizers. We compare DENTRA against a strong baseline, M2M-100, in different African multilingual machine translation scenarios and show gains in 3 out of 4 subtasks.

Document clustering requires a deep understanding of the complex structure of long-text; in particular, the intra-sentential (local) and inter-sentential features (global). Existing representation learning models do not fully capture these features. To address this, we present a novel graph-based representation for document clustering that builds a graph autoencoder (GAE) on a Keyword Correlation Graph. The graph is constructed with topical keywords as nodes and multiple local and global features as edges. A GAE is employed to aggregate the two sets of features by learning a latent representation which can jointly reconstruct them. Clustering is then performed on the learned representations, using vector dimensions as features for inducing document classes. Extensive experiments on two datasets show that the features learned by our approach can achieve better clustering performance than other existing features, including term frequency-inverse document frequency and average embedding.