--- title: "Muhammad Abdul-Mageed — papers, page 2 — Elamur Research" description: "Muhammad Abdul-Mageed: 66 papers on Elamur Research (2011–2026) — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/muhammad-abdul-mageed/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/muhammad-abdul-mageed/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) / page 2 # Muhammad Abdul-Mageed · page 2 66 papers in our library · 2011–2026 · page 2 of 3 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) [1](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) **2** [3](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/page/3/) 26. **[Dolphin: A Challenging and Diverse Benchmark for Arabic NLG](https://md.research.elamur.ai/free/en/papers/nagoudi-2023-dolphin-challenging-diverse-benchmark-arabic/)** *(2023 · 35 citations)* Arabic is a large family of varieties, from Classical Arabic through Modern Standard Arabic (MSA) to many regional dialects, and it is also written in Latin script (Arabizi). 27. **[WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task](https://md.research.elamur.ai/free/en/papers/jarrar-2023-wojoodner-2023-first-arabic-named/)** *(2023 · 31 citations)* WojoodNER-2023 was organised as the first shared task for Arabic named entity recognition. The motivation is that earlier Arabic NER work mostly handled flat mentions with only a few classes (person, organisation, location), and it relied on older news-style corpora. The organisers gave participants the Wojood… 28. **[Understanding and Detecting Dangerous Speech in Social Media](https://md.research.elamur.ai/free/en/papers/alshehri-2020-understanding-detecting-dangerous-speech-social/)** *(2020 · 31 citations)* The paper addresses the detection of dangerous speech in Arabic social media, focusing on direct threats of physical harm. 29. **[N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition](https://md.research.elamur.ai/free/en/papers/talafha-2023-n-shot-benchmarking-whisper-diverse/)** *(2023 · 30 citations)* The authors ask how well OpenAI's Whisper handles Arabic beyond the standard benchmarks. Arabic is a family of varieties: Modern Standard Arabic (MSA) sits alongside many regional dialects. They test Whisper large-v2 and small, with XLS-R as the baseline, on nearly all public Arabic ASR corpora. These cover Common… 30. **[Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation](https://md.research.elamur.ai/free/en/papers/elmadany-2023-octopus-multitask-model-toolkit-arabic/)** *(2023 · 29 citations)* Arabic natural language generation has lacked a single, broad toolkit. Available public tools mostly cover only translation into Arabic. The authors address this with AraT5 v2, a new Arabic text-to-text Transformer, and OCTOPUS, a toolkit that runs eight generation tasks from one model. AraT5 v2 was pretrained on… 31. **[Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks](https://md.research.elamur.ai/free/en/papers/alwajih-2024-peacock-family-arabic-multimodal-large/)** *(2024 · 28 citations)* Multimodal LLMs that reason over images work well mainly in English, because other languages lack good image–text resources. 32. **[WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task](https://md.research.elamur.ai/free/en/papers/jarrar-2024-wojoodner-2024-second-arabic-named/)** *(2024 · 28 citations)* WojoodNER-2024 is the second round of a shared task on Arabic named entity recognition. It moves the field past the usual few coarse classes (person, organization, location) toward a detailed type hierarchy. The organizers released Wojood Fine, a re-annotation of the Wojood corpus of about 550k tokens. It covers… 33. **[Leveraging Affective Bidirectional Transformers for Offensive Language Detection](https://md.research.elamur.ai/free/en/papers/elmadany-2020-leveraging-affective-bidirectional-transformers-offensive/)** *(2020 · 26 citations)* The paper describes a system built for the OSACT4 shared task on detecting offensive language (Sub-task A) and hate speech (Sub-task B) in Arabic tweets. 34. **[Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic](https://md.research.elamur.ai/free/en/papers/alwajih-2024-dallah-dialect-aware-multimodal-large/)** *(2024 · 25 citations)* Multimodal LLMs that can discuss images are mostly built for English. Arabic has few image-text resources, and Arabic's regional dialects have almost none. Dallah addresses this gap. It joins the LLaVA framework with AraLLaMA, a LLaMA-2-based model that handles both Arabic and English. 35. **[Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling](https://md.research.elamur.ai/free/en/papers/khalifa-2021-self-training-pre-trained-language/)** *(2021 · 25 citations)* Labeled data for Arabic NLP is mostly in Modern Standard Arabic (MSA), while everyday online writing is in dialects. 36. **[AraNet: A Deep Learning Toolkit for Arabic Social Media](https://md.research.elamur.ai/free/en/papers/mageed-2020-aranet-deep-learning-toolkit-arabic/)** *(2020 · 25 citations)* Arabic has about 400 million native speakers and many dialects, but it had no single toolkit for inferring social attributes from social media posts. 37. **[Arabic Fine-Grained Entity Recognition](https://md.research.elamur.ai/free/en/papers/liqreina-2023-arabic-fine-grained-entity-recognition/)** *(2023 · 24 citations)* Most named-entity recognizers assign only broad labels such as organization or location. This paper adds a finer layer for Arabic. Previously, the only Arabic resource with subtypes was the costly and ageing ACE2005. The authors instead extended the open Wojood corpus (550K tokens, 21 nested entity types, mostly MSA… 38. **[PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture](https://md.research.elamur.ai/free/en/papers/alwajih-2025-palmx-2025-first-shared-task/)** *(2025 · 23 citations)* Large language models learn mostly from web data, which leans toward Western, high-resource cultures. As a result, they understand Arab and Islamic customs, history and religious practice poorly, and there was no common yardstick to measure this. PalmX 2025 is a shared task built to fill that gap. It is divided into… 39. **[TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-turjuman-public-toolkit-neural-arabic/)** *(2022 · 21 citations)* Arabic NLP already had public tools for morphology and social-meaning detection, but there were few openly available machine translation tools. 40. **[Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation](https://md.research.elamur.ai/free/en/papers/nagoudi-2021-investigating-code-mixed-modern-standard/)** *(2021 · 21 citations)* This paper asks how well modern neural MT handles a source side that mixes Modern Standard Arabic with Egyptian Arabic (MSAEA) when translating into English. 41. **[Qalam: A Multimodal LLM for Arabic Optical Character and Handwriting Recognition](https://md.research.elamur.ai/free/en/papers/bhatia-2024-qalam-multimodal-llm-arabic-optical/)** *(2024 · 20 citations)* Arabic text is hard for OCR and handwriting recognition (HWR). The script is cursive, letters change shape with position, and diacritics, dots, ligatures and overlaps all have to be read. Fonts and personal hands vary widely, and annotated data is limited. The authors present Qalam, a vision encoder-decoder… 42. **[VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System](https://md.research.elamur.ai/free/en/papers/waheed-2023-voxarabica-robust-dialect-aware-arabic/)** *(2023 · 20 citations)* Arabic speech technology has mostly targeted Modern Standard Arabic (MSA). Systems built for a single dialect fail on speech from other regions. VoxArabica tackles this with a demo pipeline that first identifies the dialect of an utterance and then sends it to a matching speech recognizer. 43. **[JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking](https://md.research.elamur.ai/free/en/papers/magdy-2025-jawaher-multidialectal-dataset-arabic-proverbs/)** *(2025 · 18 citations)* Many LLMs still lean toward Anglo-American culture and handle English better than other languages. This makes culturally loaded figurative language, such as proverbs, especially hard for them. The authors present Jawaher, a manually curated benchmark of Arabic proverbs from 20 Arabic varieties spread across the Arab… 44. **[On the Robustness of Arabic Speech Dialect Identification](https://md.research.elamur.ai/free/en/papers/sullivan-2023-robustness-arabic-speech-dialect-identification/)** *(2023 · 18 citations)* Arabic dialect identification (ADI) systems often act as filters in large web-scale pipelines that gather speech for training ASR. 45. **[Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder](https://md.research.elamur.ai/free/en/papers/mohamed-2023-violet-vision-language-model-arabic/)** *(2023 · 18 citations)* Arabic image captioning lags far behind English because native captioned images are scarce and strong Arabic generators have rarely been connected to vision models. 46. **[Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding](https://md.research.elamur.ai/free/en/papers/sullivan-2022-improving-automatic-speech-recognition-non/)** *(2022 · 16 citations)* ASR systems tuned for native (L1) English perform noticeably worse on non-native (L2) speakers. Accents shift pronunciation, and little annotated accented audio exists for training. The authors start from the self-trained wav2vec 2.0 Large model (Wav2Vec 2.0-ST) and fine-tune it with CTC under many data conditions… 47. **[NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task](https://md.research.elamur.ai/free/en/papers/talafha-2025-nadi-2025-first-multidialectal-arabic/)** *(2025 · 15 citations)* NADI 2025 is the sixth edition of the Nuanced Arabic Dialect Identification series. Earlier editions worked on written text. This one moves to speech, because everyday Arabic is mostly spoken dialect, and few datasets annotate it. The shared task has three linked subtasks: 48. **[John vs. Ahmed: Debate-Induced Bias in Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/demidova-2024-john-vs-ahmed-debate-induced/)** *(2024 · 14 citations)* This paper asks whether popular chat LLMs carry identity-related preferences that alignment hides, and whether those preferences shift with the prompt language. 49. **[Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks](https://md.research.elamur.ai/free/en/papers/bhatia-2025-swan-arabicmteb-dialect-aware-arabic/)** *(2025 · 13 citations)* Most text embedding models are built mainly for English and Chinese. They handle Arabic poorly because the language combines rich morphology, many dialects, and culture-specific content. The authors address this with Swan, a pair of Arabic-focused embedding models. Swan-Small is built on ARBERTv2, and Swan-Large is… 50. **[ASMA: A System for Automatic Segmentation and Morpho-Syntactic Disambiguation of Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/mageed-2013-asma-system-automatic-segmentation-morpho/)** *(2013 · 13 citations)* Arabic packs clitics, stems and inflectional affixes into a single written word, so POS tagging requires splitting words and disambiguating their morphology together. [← prev](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) [1](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) **2** [3](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/page/3/)