Papers by Abubakr Mohamed
AraSafe: Benchmarking Safety in Arabic LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | AraSafe is the first large-scale native Arabic safety benchmark for large language models (LLMs) it addresses the pressing need for culturally and linguistically representative evaluation resources. |
| Approach: | They propose to use Arabic prompts to annotate harmful and non-harmful prompts into nine fine-grained safety categories to support classifiers for harmful content. |
| Outcome: | The proposed benchmarks address the need for culturally and linguistically representative evaluation resources. |
Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Arabic diacritics are typically omitted in written Arabic, leading to ambiguity . authors propose a methodology to analyze and refine a large diacritized corpus . |
| Approach: | They propose a methodology to analyze and refine a large diacritized corpus to improve training quality. |
| Outcome: | The proposed model achieves state-of-the-art results with 3.12% and 2.70% WER on WikiNews-2014 and Wikinews-2024. |
Nahw: A Comprehensive Benchmark of Arabic Grammar Understanding, Error Detection, Correction, and Explanation (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing corpora address individual linguistic aspects like spelling or diacritization, but rarely provide explanations of grammatical errors. Existing datasets and benchmarks that capture Arabic's grammatological complexity are scarce. |
| Approach: | They propose a benchmark for Arabic grammar that covers error detection, correction, and explanation. |
| Outcome: | The proposed model performs better on GPT-4o than on the best performing model (ALLaM-7B) despite fine tuning with synthetic data, the model perform better on Arabic grammar tasks. |