---
title: "Saudi Arabia — research institutions, scientists and papers"
description: "Saudi Arabia: 18 institutions with papers in our library in 9 agglomerations, 46 people with a page, 99 works, 6,474 citations."
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---
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# Saudi Arabia

Country 18 institutions in 9 agglomerations · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**18** institutions with a page](https://md.research.elamur.ai/free/en/countries/saudi-arabia/#institutions) · [**9** agglomerations](https://md.research.elamur.ai/free/en/countries/saudi-arabia/#agglomerations) · [**46** people with a page](https://md.research.elamur.ai/free/en/countries/saudi-arabia/#authors) · [**99** works in our library](https://md.research.elamur.ai/free/en/countries/saudi-arabia/#works) · [**6,474** citations of their works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/#works)

## About

### Profile

Saudi Arabia appears in this library as a cluster of universities, one hospital and one company. Their papers deal almost entirely with language: how computers read, write, translate and evaluate Arabic, and how large language models behave. Most of the country's institutions on this site are universities. They are spread from the Gulf coast to the Red Sea and into the interior. The heaviest concentrations are in the Eastern Province agglomeration around [Dammam](https://md.research.elamur.ai/free/en/agglomerations/dammam/), where Dhahran and Khobar sit, in [Jeddah](https://md.research.elamur.ai/free/en/agglomerations/jeddah/) on the west coast, and in Riyadh.

The topic profile is narrow and sharply defined. Nearly every work here is filed under [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/), and the field shelf is dominated by linguistics and languages, followed by natural language processing, machine translation, artificial intelligence, law and a single mathematics paper. Running across this Arabic-centred core is a smaller body of very widely cited work on [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). It includes multilingual instruction tuning, surveys of large models and benchmarks that test frontier systems. Much of the country's citation weight on this site comes from these broader papers. The Arabic-specific work makes up most of its volume.

### History

Among the works here, Saudi research begins in 2010 with a statistical study of clitics in Arabic, affiliated with [King Saud University](https://md.research.elamur.ai/free/en/universities/king-saud-university/). It counts how prefixes such as the article and conjunction, and suffixes such as attached pronouns, behave in written text. A gap follows. The next work is a 2016 study at [Umm al-Qura University](https://md.research.elamur.ai/free/en/universities/umm-al-qura-university/) that compared statistical methods for pulling multi-word formulaic sequences out of Arabic corpora. In 2018 came an annotation project at [Imam Mohammad ibn Saud Islamic University](https://md.research.elamur.ai/free/en/universities/imam-mohammad-ibn-saud-islamic-university/) on attribution relations, which records who said or believed what in Arabic text.

From 2019 the pace picks up, and social media becomes the main source material. In 2019 and 2020, groups at Umm al-Qura University, [Jazan University](https://md.research.elamur.ai/free/en/universities/jazan-university/), [Taibah University](https://md.research.elamur.ai/free/en/universities/taibah-university/), [Taif University](https://md.research.elamur.ai/free/en/universities/taif-university/) and [King Abdulaziz University](https://md.research.elamur.ai/free/en/universities/king-abdulaziz-university/) built Twitter corpora. These covered sentiment, speech acts, religious hate speech spread by bots, COVID-19 discussion, and Saudi tweets about floods, dust storms and traffic accidents. Dialect identification and rule-based morphology also appear in these years.

The years 2021 to 2023 bring the shift to large-scale pretrained models. [King Fahd University of Petroleum and Minerals](https://md.research.elamur.ai/free/en/universities/king-fahd-university-of-petroleum-and-minerals/) is linked to multilingual instruction-tuning work in 2021 and 2022. In 2023 a broad survey of large language models and the BigScience ROOTS corpus follow. At [King Abdullah University of Science and Technology](https://md.research.elamur.ai/free/en/universities/king-abdullah-university-of-science-and-technology/), 2023 brings work on vision and language and on detecting machine-generated text.

From 2024 onward the Arabic and the general lines meet. Several Arabic-native or Arabic-localised models, Arabic benchmarks, shared tasks and Arabic safety evaluations appear. The most recent works, dated 2026, cover dialectal speech recognition, pronunciation assessment and safety testing across dialects.

### Research profile

The central field is Arabic natural language processing, and within it the largest single strand on this site is [sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/). Work at Jazan University studied feature selection for dialectal Arabic sentiment with support vector machines (2019) and rule-based handling of negation in colloquial Arabic. A later paper examined how emoji carry sentiment in ironic Arabic posts. The ASAD benchmark of 2020, shared by King Abdullah University of Science and Technology and King Abdulaziz University, offered a Twitter-based sentiment collection. Taif University contributed ArPanEmo, a fine-grained emotion corpus, and EHSAN, which used ChatGPT within a hybrid framework for aspect-based sentiment in Google Maps reviews of hospitals. King Saud University and Imam Mohammad ibn Saud Islamic University shared MultiProSE, which labels propaganda, sentiment and emotion together.

A second strand concerns harmful and sensitive content. It includes suicidality detection in Arabic tweets at King Abdulaziz University, cyberbullying detection at Taibah University, bot-driven religious hate speech at Taibah and Taif, spam detection in tweets about a telecom operator at Imam Abdulrahman Bin Faisal University, and persuasion techniques at Umm al-Qura University. The fact-checking system Ta'keed, from King Abdulaziz University, brings retrieval-backed claim verification to Arabic.

Dialects form a third line. Maha Althobaiti's survey of written dialect identification at Taif University is a reference point here. A study at Prince Sattam Bin Abdulaziz University asked whether geographic closeness predicts dialect similarity. King Saud University produced a dialect-aware classification framework. Taibah University released pretrained models for Saudi and Egyptian dialects, and King Abdullah University of Science and Technology worked on parameter-efficient identification of spoken dialects.

A fourth line is language models built for or tested on Arabic. AceGPT localised a large model to Arabic on top of LLaMA2. ArabianGPT at Prince Sultan University argued for generative models designed around Arabic itself. ArabicMMLU built a multitask benchmark from material native to the Arab world rather than translated tests. Taqyim assessed ChatGPT models on Arabic tasks. Further work covered Matryoshka-style sentence embeddings, reverse dictionaries and a new approach to evaluating Arabic models with hand-written questions drawn from books. This strand overlaps with [evaluation and benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), which also covers essay grading by large models at Umm al-Qura University and the SalamahBench safety benchmark at Taibah University.

Smaller lines fill out the picture. Grammatical error correction appears in the Tibyan corpus and in a 2025 study of balanced synthetic training material, both from King Abdulaziz University. Legal judgment prediction is represented by work on personal status cases and on forecasting court rulings with large models. There is also morphology and tokenization, coreference annotation through a virtual-world game, a math word problem collection, synthetic images for classifying Old Aramaic letters, and a theoretical linguistics paper on serial verb constructions across Arabic, Hebrew and Aramaic. Speech appears through a pronunciation aid for children, the Bulbul dialectal speech collection and the IQRA 2026 challenge.

The general machine learning strand stands somewhat apart. It covers multitask prompted training, crosslingual generalisation, surveys of large models and of multi-agent systems, vision-language models, detection of machine-generated text, and a benchmark built to test frontier models.

### Leading scientists

[Zaid Alyafeai](https://md.research.elamur.ai/free/en/authors/zaid-alyafeai/), at King Fahd University of Petroleum and Minerals, comes first among the country's people on this site. The work here centres on Arabic language resources and their evaluation. It includes [Evaluating Various Tokenizers for Arabic Text Classification](https://md.research.elamur.ai/free/en/papers/alyafeai-2021-evaluating-various-tokenizers-arabic-text/), which proposed stochastic, disjoint-letter and morphological tokenizers. It also includes the Masader catalogue of Arabic text and speech resources, the Taqyim evaluation of ChatGPT on Arabic tasks, and co-authorship of the StanceEval 2024 shared task.

[Mohamed Elhoseiny](https://md.research.elamur.ai/free/en/authors/mohamed-elhoseiny/) heads the King Abdullah University of Science and Technology group on this site. Its works include the vision-language model MiniGPT-v2. Three of that paper's named authors follow next: [Jun Chen](https://md.research.elamur.ai/free/en/authors/jun-chen/), [Deyao Zhu](https://md.research.elamur.ai/free/en/authors/deyao-zhu/) and [Xiaoqian Shen](https://md.research.elamur.ai/free/en/authors/xiaoqian-shen/). Their shared paper here showed that one simple model design can act as a common interface for many vision-language tasks.

[Mingchen Zhuge](https://md.research.elamur.ai/free/en/authors/mingchen-zhuge/) belongs to the same university, whose works in this period span multimodal models and agent systems. [Di Wang](https://md.research.elamur.ai/free/en/authors/di-wang/) is a co-author of DetectLLM, which scores text by token log-rank to spot machine-generated passages without training a detector. [Xiangliang Zhang](https://md.research.elamur.ai/free/en/authors/xiangliang-zhang/) has the broadest set of papers among the university's people here. These include ArMATH, a collection of Arabic math word problems, built because earlier work on the task had focused on English and Chinese. [Bernard Ghanem](https://md.research.elamur.ai/free/en/authors/bernard-ghanem/) and [Bang An](https://md.research.elamur.ai/free/en/authors/bang-an/) complete the group from that university among the first names.

[Emad A. Alghamdi](https://md.research.elamur.ai/free/en/authors/emad-a-alghamdi/) is the first King Abdulaziz University scientist shown. That university's works range from multilingual instruction tuning to Arabic grammatical error correction and legal prediction. [Hamzah Luqman](https://md.research.elamur.ai/free/en/authors/hamzah-luqman/), at King Fahd University of Petroleum and Minerals, co-organised StanceEval 2024, the first shared task on Arabic stance detection.

Beyond these first names, the institutions show further people with distinct lines. [Anis Koubaa](https://md.research.elamur.ai/free/en/authors/anis-koubaa/), affiliated with Prince Sultan University and Imam Mohammad ibn Saud Islamic University, led ArabianGPT and co-authored the legal-ruling prediction study. [Faisal Qarah](https://md.research.elamur.ai/free/en/authors/faisal-qarah/) at Taibah University produced SaudiBERT, EgyBERT and AraPoemBERT, a model pretrained only on Arabic poetry. [Maha Althobaiti](https://md.research.elamur.ai/free/en/authors/maha-althobaiti/) at Taif University wrote the dialect identification survey and the ArPanEmo emotion corpus.

### Landmark works

The most cited work linked to Saudi Arabia here is [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/) (2021), affiliated with King Fahd University of Petroleum and Minerals. It addressed the observation that zero-shot ability appears only at large scale and depends on prompt wording, and it trained models on many tasks phrased as prompts.

Its follow-up, [Crosslingual Generalization through Multitask Finetuning](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/) (2022), tested whether that approach carries over to multilingual settings. Between them sits [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) (2023), a survey that offers no new model or corpus but organises a very large literature.

From King Abdullah University of Science and Technology, [Large Language Model based Multi-Agents](https://md.research.elamur.ai/free/en/papers/guo-2024-large-language-model-based/) (2024) surveys multi-agent systems along four dimensions, starting with how agents connect to their environment. [MiniGPT-v2](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/) (2023) and DetectLLM (2023) are also among the most cited works here.

[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) (2025), affiliated with King Saud University, responds to the near-saturation of benchmarks such as MMLU with a much harder test. The Aya Dataset (2024), shared by King Fahd University of Petroleum and Minerals and King Abdulaziz University, assembled open instruction-tuning material beyond English. The BigScience ROOTS corpus (2023) describes how a large multilingual training collection was built.

Two Arabic-focused works follow closely: [AceGPT](https://md.research.elamur.ai/free/en/papers/huang-2024-acegpt-localizing-large-language-models/) (2024) and ArabicMMLU (2024). Among the papers led from inside the country, the tokenizer study, the COVID-19 Twitter corpus from Umm al-Qura University and the dialect-proximity study from Prince Sattam Bin Abdulaziz University are the most cited.

### Labs and units

The country's presence on this site is organised by institution. King Fahd University of Petroleum and Minerals in Dhahran has the largest body of works and the most citations among them. King Abdullah University of Science and Technology in Jeddah has a smaller set whose papers are heavily drawn on. King Abdulaziz University in Jeddah, King Saud University and [Prince Sultan University](https://md.research.elamur.ai/free/en/universities/prince-sultan-university/) in Riyadh each hold a sizeable group.

[Prince Sattam Bin Abdulaziz University](https://md.research.elamur.ai/free/en/universities/prince-sattam-bin-abdulaziz-university/) in Al Kharj has few works, but they are well cited. Umm al-Qura University in Mecca, Taibah University in Medina, Taif University, Jazan University, [Imam Abdulrahman Bin Faisal University](https://md.research.elamur.ai/free/en/universities/imam-abdulrahman-bin-faisal-university/) in Dammam and Imam Mohammad ibn Saud Islamic University each hold smaller sets. So do Alfaisal University, Saudi Electronic University, Qassim University in Buraydah and Tuwaiq Academy.

Outside the universities, [Saudi Aramco](https://md.research.elamur.ai/free/en/companies/saudi-aramco/) appears in Dhahran and [Saad Specialist Hospital](https://md.research.elamur.ai/free/en/hospitals/saad-specialist-hospital/) in Khobar, each with a small group of works.

By agglomeration, the Dammam region carries the most citation weight, mainly through the Dhahran university. Jeddah comes next. [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/) has the most institutions.

### Collaborations

Many works here are shared between Saudi institutions. The ROOTS corpus and ArabicMMLU link King Fahd University of Petroleum and Minerals with Prince Sattam Bin Abdulaziz University. The Aya Dataset links the Dhahran university with King Abdulaziz University. ASAD connects King Abdullah University of Science and Technology and King Abdulaziz University in Jeddah. Arabic Tweet-Act joins Umm al-Qura University and Jazan University. The study of hateful bots joins Taibah University and Taif University. MultiProSE is shared by King Saud University and Imam Mohammad ibn Saud Islamic University. The children's pronunciation tool connects Prince Sultan University and Imam Mohammad ibn Saud Islamic University.

People also bridge institutions. Anis Koubaa and Adel Ammar appear at both Prince Sultan University and Imam Mohammad ibn Saud Islamic University. Serry Sibaee is shown at Prince Sultan University and King Abdulaziz University. Bilel Benjdira links Prince Sultan University with King Saud University, and Wadii Boulila links it with Taibah University. Ahlam Alrehili, whose grammatical error correction work comes from King Abdulaziz University, also appears at Taibah University. Aisha Alansari is shown at both King Fahd University of Petroleum and Minerals and Imam Abdulrahman Bin Faisal University.

The institutions' people also include co-authors such as Eric Atwell, Philip S. Yu and Min Chen at King Abdulaziz University, and Victor O. K. Li and Abdulmotaleb El Saddik at King Saud University. These names point to collaboration beyond the kingdom's own staff. The large multilingual projects, from multitask prompted training to Aya and ROOTS, were broad international efforts in which Saudi affiliations formed one part.

Industry and academia sit side by side in Dhahran, where Saudi Aramco shares a city with King Fahd University of Petroleum and Minerals. Clinical work appears through the hospital in Khobar within the same agglomeration. On this site, however, both of these non-university contributors have only small sets of works.

### Timeline

From 2010 to 2018 the works here are few and scattered. They comprise a statistical study of clitics at King Saud University, a 2016 comparison of formulaic sequence extraction at Umm al-Qura University, and a 2018 annotation of attribution relations at Imam Mohammad ibn Saud Islamic University. All of them concern the structure of written Arabic.

The years 2019 and 2020 bring social-media corpora and classical machine learning. Work in this period covers dialectal sentiment and negation at Jazan University, speech acts in tweets, and bot-spread hate speech. It also includes ASAD, the COVID-19 Twitter collection, the FloDusTA event corpus, a dialect identification survey, a finite-state morphological analyser, and an opinion-mining recommender approach.

From 2021 to 2022 the large-model era arrives in the country's works. Multitask prompted training and its crosslingual follow-up date from these years, alongside the Arabic tokenizer study, the dialect-proximity study and the Masader resource catalogue with its Masader Plus interface. ArMATH, a comparison of Arabic named entity tools, probes of linguistic knowledge in Arabic BERT models, a challenge set in Classical Arabic and game-based coreference annotation also appear.

The year 2023 adds the broad survey of large models, the ROOTS corpus, MiniGPT-v2 and DetectLLM. Arabic work in the same year covers legal judgment prediction, suicidality detection, emotion and sarcasm corpora, Taqyim, Old Aramaic letter classification and spoken dialect identification.

In 2024 the Arabic and general lines draw closer. AceGPT, ArabicMMLU, ArabianGPT and the Saudi, Egyptian and poetry BERT models appear, together with StanceEval, NADI submissions, the Tibyan corpus, Ta'keed and the Aya Dataset.

The years 2025 and 2026 continue with evaluation and safety. Humanity's Last Exam, the AraGenEval shared task on authorship style transfer and detection of generated text, essay grading by large models, reverse dictionaries and propaganda labelling date from 2025. The most recent works, from 2026, include a benchmark for dialect-level safety, a dialectal speech collection and an Interspeech challenge on pronunciation assessment.

## Where

## Agglomerations (9)

1. [**Dammam**4 institutions*4,332 citations*](https://md.research.elamur.ai/free/en/agglomerations/dammam/)
2. [**Jeddah**2 institutions*1,165 citations*](https://md.research.elamur.ai/free/en/agglomerations/jeddah/)
3. [**Riyadh**6 institutions*422 citations*](https://md.research.elamur.ai/free/en/agglomerations/riyadh/)
4. [**Mecca**1 institution*87 citations*](https://md.research.elamur.ai/free/en/agglomerations/mecca/)
5. [**Medina**1 institution*52 citations*](https://md.research.elamur.ai/free/en/agglomerations/medina/)
6. [**Al-Kharj**1 institution*307 citations*](https://md.research.elamur.ai/free/en/agglomerations/al-kharj/)
7. [**Jizan**1 institution*60 citations*](https://md.research.elamur.ai/free/en/agglomerations/jizan/)
8. [**Ta'if**1 institution*42 citations*](https://md.research.elamur.ai/free/en/agglomerations/ta-if/)
9. [**Buraydah**1 institution*7 citations*](https://md.research.elamur.ai/free/en/agglomerations/buraydah/)

## Institutions (18)

### 🎓 Universities (16)

1. **[King Fahd University of Petroleum and Minerals](https://md.research.elamur.ai/free/en/universities/king-fahd-university-of-petroleum-and-minerals/)**University · [Dhahran](https://md.research.elamur.ai/free/en/agglomerations/dammam/), Saudi Arabia*23 works · 4,250 citations*
2. **[King Abdullah University of Science and Technology](https://md.research.elamur.ai/free/en/universities/king-abdullah-university-of-science-and-technology/)**University · [Jeddah](https://md.research.elamur.ai/free/en/agglomerations/jeddah/), Saudi Arabia*9 works · 856 citations*
3. **[King Abdulaziz University](https://md.research.elamur.ai/free/en/universities/king-abdulaziz-university/)**University · [Jeddah](https://md.research.elamur.ai/free/en/agglomerations/jeddah/), Saudi Arabia*12 works · 309 citations*
4. **[Prince Sultan University](https://md.research.elamur.ai/free/en/universities/prince-sultan-university/)**University · [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/), Saudi Arabia*12 works · 73 citations*
5. **[King Saud University](https://md.research.elamur.ai/free/en/universities/king-saud-university/)**University · [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/), Saudi Arabia*13 works · 290 citations*
6. **[Umm al-Qura University](https://md.research.elamur.ai/free/en/universities/umm-al-qura-university/)**University · [Makkah al Mukarramah](https://md.research.elamur.ai/free/en/agglomerations/mecca/), Saudi Arabia*7 works · 87 citations*
7. **[Taibah University](https://md.research.elamur.ai/free/en/universities/taibah-university/)**University · [Medina](https://md.research.elamur.ai/free/en/agglomerations/medina/), Saudi Arabia*8 works · 52 citations*
8. **[Prince Sattam Bin Abdulaziz University](https://md.research.elamur.ai/free/en/universities/prince-sattam-bin-abdulaziz-university/)**University · [Al Kharj](https://md.research.elamur.ai/free/en/agglomerations/al-kharj/), Saudi Arabia*4 works · 307 citations*
9. **[Jazan University](https://md.research.elamur.ai/free/en/universities/jazan-university/)**University · [Gazan](https://md.research.elamur.ai/free/en/agglomerations/jizan/), Saudi Arabia*4 works · 60 citations*
10. **[Taif University](https://md.research.elamur.ai/free/en/universities/taif-university/)**University · [Ta'if](https://md.research.elamur.ai/free/en/agglomerations/ta-if/), Saudi Arabia*4 works · 42 citations*
11. **[Imam Abdulrahman Bin Faisal University](https://md.research.elamur.ai/free/en/universities/imam-abdulrahman-bin-faisal-university/)**University · [Dammam](https://md.research.elamur.ai/free/en/agglomerations/dammam/), Saudi Arabia*2 works · 40 citations*
12. **[Imam Mohammad ibn Saud Islamic University](https://md.research.elamur.ai/free/en/universities/imam-mohammad-ibn-saud-islamic-university/)**University · [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/), Saudi Arabia*6 works · 12 citations*
13. **[Alfaisal University](https://md.research.elamur.ai/free/en/universities/alfaisal-university/)**University · [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/), Saudi Arabia*3 works · 21 citations*
14. **[Saudi Electronic University](https://md.research.elamur.ai/free/en/universities/saudi-electronic-university/)**University · [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/), Saudi Arabia*2 works · 25 citations*
15. **[Qassim University](https://md.research.elamur.ai/free/en/universities/qassim-university/)**University · [Buraidah](https://md.research.elamur.ai/free/en/agglomerations/buraydah/), Saudi Arabia*3 works · 7 citations*
16. **[Tuwaiq Academy](https://md.research.elamur.ai/free/en/universities/tuwaiq-academy/)**University · [Riyadh](https://md.research.elamur.ai/free/en/agglomerations/riyadh/), Saudi Arabia*3 works · 1 citation*

### 🏢 Companies (1)

1. **[Saudi Aramco](https://md.research.elamur.ai/free/en/companies/saudi-aramco/)**Company · [Dhahran](https://md.research.elamur.ai/free/en/agglomerations/dammam/), Saudi Arabia*2 works · 35 citations*

### 🏥 Hospitals and medical centres (1)

1. **[Saad Specialist Hospital](https://md.research.elamur.ai/free/en/hospitals/saad-specialist-hospital/)**Hospital · [Khobar](https://md.research.elamur.ai/free/en/agglomerations/dammam/), Saudi Arabia*3 works · 7 citations*

## People (46)

Authors with a page confirmed at its institutions, the most cited first.

1. [**Zaid Alyafeai**King Fahd University of Petroleum and Minerals*13 papers here · 3,152 citations*](https://md.research.elamur.ai/free/en/authors/zaid-alyafeai/)
2. [**Mohamed Elhoseiny**King Abdullah University of Science and Technology*2 papers here · 2,888 citations*](https://md.research.elamur.ai/free/en/authors/mohamed-elhoseiny/)
3. [**Jun Chen**King Abdullah University of Science and Technology*2 papers here · 1,483 citations*](https://md.research.elamur.ai/free/en/authors/jun-chen/)
4. [**Deyao Zhu**King Abdullah University of Science and Technology*2 papers here · 1,361 citations*](https://md.research.elamur.ai/free/en/authors/deyao-zhu/)
5. [**Xiaoqian Shen**King Abdullah University of Science and Technology*2 papers here · 1,297 citations*](https://md.research.elamur.ai/free/en/authors/xiaoqian-shen/)
6. [**Mingchen Zhuge**King Abdullah University of Science and Technology*2 papers here · 1,140 citations*](https://md.research.elamur.ai/free/en/authors/mingchen-zhuge/)
7. [**Di Wang**King Abdullah University of Science and Technology*2 papers here · 972 citations*](https://md.research.elamur.ai/free/en/authors/di-wang/)
8. [**Xiangliang Zhang**King Abdullah University of Science and Technology*5 papers here · 781 citations*](https://md.research.elamur.ai/free/en/authors/xiangliang-zhang/)
9. [**Bernard Ghanem**King Abdullah University of Science and Technology*2 papers here · 714 citations*](https://md.research.elamur.ai/free/en/authors/bernard-ghanem/)
10. [**Bang An**King Abdullah University of Science and Technology*2 papers here · 304 citations*](https://md.research.elamur.ai/free/en/authors/bang-an/)
11. [**Emad A. Alghamdi**King Abdulaziz University*2 papers here · 277 citations*](https://md.research.elamur.ai/free/en/authors/emad-a-alghamdi/)
12. [**Hamzah Luqman**King Fahd University of Petroleum and Minerals*5 papers here · 166 citations*](https://md.research.elamur.ai/free/en/authors/hamzah-luqman/)
13. [**Serry Sibaee**Prince Sultan University*14 papers here · 154 citations*](https://md.research.elamur.ai/free/en/authors/serry-sibaee/)
14. [**Maha Althobaiti**Taif University*2 papers here · 153 citations*](https://md.research.elamur.ai/free/en/authors/maha-althobaiti/)
15. [**Jianqing Zhu**King Abdullah University of Science and Technology*2 papers here · 152 citations*](https://md.research.elamur.ai/free/en/authors/jianqing-zhu/)
16. [**Juncai He**King Abdullah University of Science and Technology*2 papers here · 152 citations*](https://md.research.elamur.ai/free/en/authors/juncai-he/)
17. [**Mustafa Ghaleb**King Fahd University of Petroleum and Minerals*3 papers here · 148 citations*](https://md.research.elamur.ai/free/en/authors/mustafa-ghaleb/)
18. [**Saad Ezzini**King Fahd University of Petroleum and Minerals*5 papers here · 134 citations*](https://md.research.elamur.ai/free/en/authors/saad-ezzini/)
19. [**Anis Koubaa**Prince Sultan University*8 papers here · 132 citations*](https://md.research.elamur.ai/free/en/authors/anis-koubaa/)
20. [**Omer Nacar**Prince Sultan University*11 papers here · 120 citations*](https://md.research.elamur.ai/free/en/authors/omer-nacar/)
21. [**Irfan Ahmad**King Fahd University of Petroleum and Minerals*3 papers here · 95 citations*](https://md.research.elamur.ai/free/en/authors/irfan-ahmad/)
22. [**Nada Almarwani**Taibah University*3 papers here · 95 citations*](https://md.research.elamur.ai/free/en/authors/nada-almarwani/)
23. [**Adel Ammar**Prince Sultan University*9 papers here · 92 citations*](https://md.research.elamur.ai/free/en/authors/adel-ammar/)
24. [**Moataz Ahmed**King Fahd University of Petroleum and Minerals*2 papers here · 65 citations*](https://md.research.elamur.ai/free/en/authors/moataz-ahmed/)
25. [**Abdulrahman Aloraini**Qassim University*6 papers here · 63 citations*](https://md.research.elamur.ai/free/en/authors/abdulrahman-aloraini/)

[See all 46 people →](https://md.research.elamur.ai/free/en/countries/saudi-arabia/people/)

## Works in our library (99)

Works of its institutions, the most cited first.

1. [**Multitask Prompted Training Enables Zero-Shot Task Generalization**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,577 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
2. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
3. [**Crosslingual Generalization through Multitask Finetuning**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 692 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
4. [**Large Language Model based Multi-Agents: A Survey of Progress and Challenges**Taicheng Guo, Xiuying Chen, Yaqi Wang, et al.*2024 · 282 citations*](https://md.research.elamur.ai/free/en/papers/guo-2024-large-language-model-based/)
5. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
6. [**Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 202 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/)
7. [**MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning**Jun Chen, Deyao Zhu, Xiaoqian Shen, et al.*2023 · 192 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
8. [**DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/)
9. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
10. [**AceGPT, Localizing Large Language Models in Arabic**Huang Huang, Fei Yu, Jianqing Zhu, et al.*2024 · 127 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-acegpt-localizing-large-language-models/)
11. [**ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic**Fajri Koto, Haonan Li, Sara Shatnawi, et al.*2024 · 116 citations*](https://md.research.elamur.ai/free/en/papers/koto-2024-arabicmmlu-assessing-massive-multitask-language/)
12. [**Evaluating Various Tokenizers for Arabic Text Classification**Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, et al.*2021 · 66 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2021-evaluating-various-tokenizers-arabic-text/)
13. [**Large Arabic Twitter Dataset on COVID-19**Sarah Alqurashi, Ahmad Alhindi, Eisa Alanazi*2020 · 46 citations*](https://md.research.elamur.ai/free/en/papers/alqurashi-2020-large-arabic-twitter-dataset-covid/)
14. [**Similarities between Arabic Dialects: Investigating Geographical Proximity**Abdulkareem Alsudais, Wafa Alotaibi, Faye Alomary*2021 · 42 citations*](https://md.research.elamur.ai/free/en/papers/alsudais-2021-similarities-between-arabic-dialects-investigating/)
15. [**ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset**Basma Alharbi, Hind Alamro, Manal Alshehri, et al.*2020 · 39 citations*](https://md.research.elamur.ai/free/en/papers/alharbi-2020-asad-twitter-based-benchmark-arabic/)
16. [**The AraGenEval Shared Task on Arabic Authorship Style Transfer and AI Generated Text Detection**Shadi Abudalfa, Saad Ezzini, Ahmed Abdelali, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/abudalfa-2025-arageneval-shared-task-arabic-authorship/)
17. [**Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques**Asma Abdulsalam, Areej Alhothali, Saleh Al-Ghamdi*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/abdulsalam-2023-detecting-suicidality-arabic-tweets-using/)
18. [**Masader: Metadata Sourcing for Arabic Text and Speech Data Resources**Zaid Alyafeai, Maraim Masoud, Mustafa Ghaleb, et al.*2022 · 28 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2022-masader-metadata-sourcing-arabic-text/)
19. [**A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine**Omar Al-Harbi*2019 · 27 citations*](https://md.research.elamur.ai/free/en/papers/al-harbi-2019-comparative-study-feature-selection-methods/)
20. [**Prediction of Arabic Legal Rulings using Large Language Models**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/)
21. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
22. [**Automatic Arabic Dialect Identification Systems for Written Texts: A Survey**Maha J. Althobaiti*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/althobaiti-2020-automatic-arabic-dialect-identification-systems/)
23. [**StanceEval 2024: The First Arabic Stance Detection Shared Task**Nora Alturayeif, Hamzah Luqman, Zaid Alyafeai, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/alturayeif-2024-stanceeval-2024-first-arabic-stance/)
24. [**Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model**Abrar Alotaibi, Atta-ur Rahman, Raheel Alhaza, et al.*2022 · 19 citations*](https://md.research.elamur.ai/free/en/papers/alotaibi-2026-spam-sentiment-detection-arabic-tweets/)
25. [**Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations**Bushra Algotiml, AbdelRahim Elmadany, Walid Magdy*2019 · 18 citations*](https://md.research.elamur.ai/free/en/papers/algotiml-2019-arabic-tweet-act-speech-act/)

[See all 99 works →](https://md.research.elamur.ai/free/en/countries/saudi-arabia/works/)

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

💡 Concepts 1[Subword tokenization · 1 work](https://md.research.elamur.ai/free/en/concepts/subword-tokenization/)

📚 Shelves 26[Computer Science · 98 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 95 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 82 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 81 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 12 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Social Sciences · 15 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Psychology · 13 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Information Systems · 8 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Sociology and Political Science · 6 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · [Experimental and Cognitive Psychology · 8 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Mathematics · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Social Psychology · 6 works](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/) · [Communication · 2 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/communication/) · [Education · 3 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/education/) · [Human-Computer Interaction · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/human-computer-interaction/)+11 more

🧭 Topics 26[Semitic languages · 89 works](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Language models & pretraining · 17 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Multilingual & cross-lingual NLP · 3 works](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Sentiment & emotion · 14 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Vision & language · 2 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Morphology · 2 works](https://md.research.elamur.ai/free/en/topics/morphology/) · [Legal AI · 2 works](https://md.research.elamur.ai/free/en/topics/legal-ai/) · [Speech & spoken language · 3 works](https://md.research.elamur.ai/free/en/topics/speech/) · [Tokenization & segmentation · 1 work](https://md.research.elamur.ai/free/en/topics/tokenization/) · [Speech act theory · 2 works](https://md.research.elamur.ai/free/en/topics/speech-act-theory/) · [Evaluation & benchmarks · 5 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Summarization · 1 work](https://md.research.elamur.ai/free/en/topics/summarization/) · [Grammatical error correction & learner language · 3 works](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · [Machine translation · 5 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)+11 more

## Works by year

### 2020s

[**2026** 17 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2026/) · [**2025** 20 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2025/) · [**2024** 20 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2024/) · [**2023** 14 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2023/) · [**2022** 11 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2022/) · [**2021** 4 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2021/) · [**2020** 7 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2020/)

### 2010s

[**2019** 3 works](https://md.research.elamur.ai/free/en/countries/saudi-arabia/year/2019/) · [**2018** 1 work](https://md.research.elamur.ai/free/en/papers/alsaif-2018-annotating-attribution-relations-arabic/) · [**2016** 1 work](https://md.research.elamur.ai/free/en/papers/alghamdi-2016-empirical-study-arabic-formulaic-sequence/) · [**2010** 1 work](https://md.research.elamur.ai/free/en/papers/alotaiby-2010-clitics-arabic-language-statistical-study/)

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Updated 23 October 2026