---
title: "Lebanon — research institutions, scientists and papers"
description: "Lebanon: 2 institutions with papers in our library in 1 agglomerations, 10 people with a page, 20 works, 1,863 citations."
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---
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# Lebanon

Country 2 institutions in 1 agglomeration · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**2** institutions with a page](https://md.research.elamur.ai/free/en/countries/lebanon/#institutions) · [**1** agglomeration](https://md.research.elamur.ai/free/en/countries/lebanon/#agglomerations) · [**10** people with a page](https://md.research.elamur.ai/free/en/countries/lebanon/#authors) · [**20** works in our library](https://md.research.elamur.ai/free/en/countries/lebanon/#works) · [**1,863** citations of their works](https://md.research.elamur.ai/free/en/countries/lebanon/#works)

## About

### Profile

Lebanon appears on this site through a compact body of research that is almost entirely about the Arabic language and its computational treatment. Its work here comes from two universities, both in the [Beirut](https://md.research.elamur.ai/free/en/agglomerations/beirut/) agglomeration: the [American University of Beirut](https://md.research.elamur.ai/free/en/universities/american-university-of-beirut/) and the [Lebanese American University](https://md.research.elamur.ai/free/en/universities/lebanese-american-university/). Beirut is the only city region through which the country is represented. A portrait of Lebanese research on this site is therefore in practice a portrait of these two Beirut campuses and the people connected with them. Both appear among the country's institutions and its agglomerations, and Lebanon can be compared with other entries in the index of [all countries](https://md.research.elamur.ai/free/en/countries/).

The American University of Beirut accounts for most of the works and nearly all of the citations attached to Lebanon here. The Lebanese American University contributes a smaller group of papers, mostly recent, on spelling correction, spoken command recognition and Romanized Arabic. Every work tied to the country on this site carries the topic [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/). Nearly all of them sit on the [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) shelf, with a smaller presence on the [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) shelf. The overall picture is one of a tightly focused research community: Arabic, in its standard and dialectal forms, studied with the methods of language technology.

### History

The oldest work connected with Lebanon on this site dates from 2012. It is a morphological analyzer for Arabic by [Fadi Zaraket](https://md.research.elamur.ai/free/en/authors/fadi-zaraket/) and Jad Makhlouta. It reflects an approach in which Arabic processing rested on hand-built rules describing how prefixes, stems and suffixes combine. That paper stands somewhat alone in time, and the next works appear only later in the decade.

In 2019 the focus moves toward annotated resources for social media text, with a corpus of Levantine Arabic tweets labelled for sentiment toward specific targets. A year later, in 2020, the shift to neural pretraining arrives with AraBERT, a transformer model trained for Arabic, which is by far the most cited Lebanese work here. That paper marks a turning point. Much of the later research builds on large pretrained models, whether by adapting them, evaluating them or probing how safely they behave.

From 2023 the range widens. Work appears on a morphologically annotated corpus of Syrian Arabic, on text-to-speech built from broadcast news recordings, and on spoken command spotting. In 2024 come a native Arabic benchmark of science and technology questions for evaluating large language models and a deep learning approach to Arabic spelling correction.

The year 2026 brings the largest single cluster of papers. These are studies of contamination hidden by translation, extractive summarization, Romanized Arabic across dialects, a fact-checking collection generated with language models, suicide risk assessment from helpline calls, and safety alignment through selective refusal. Read in sequence, these works trace a path from rule-based morphology through resource building and pretraining to the evaluation, reliability and social uses of large language models.

### Research profile

The single thread running through the whole body of work is Arabic. Within it, several distinct lines can be traced among the works here.

The first is morphology and dialect description. Arabic word structure is the subject of the 2012 analyzer by Zaraket and Makhlouta. It treats affixes as agglutinative morphemes joined by fusional concatenation rules, rather than enumerating every possible pairing of prefix and suffix. The same interest returns in 2023 with [Nâbra: Syrian Arabic Dialects with Morphological Annotations](https://md.research.elamur.ai/free/en/papers/nayouf-2023-nabra-syrian-arabic-dialects-morphological/), a corpus of Syrian Arabic drawn largely from social media and annotated word by word. Both papers carry the [Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) topic. Together they show a long-running concern with the internal structure of Arabic words, standard and dialectal.

The second line is pretrained language models. [AraBERT: Transformer-based Model for Arabic Language Understanding](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/) starts from the observation that Arabic combines intricate word formation with relatively scarce resources. Its script is also unlike the Latin-script languages for which many models were first built. The paper belongs to the topic of [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/). A later paper extends it directly. [Extractive Summarization for Arabic Documents Using SAraBERT with a Semantic Siamese Similarity Evaluation Metric](https://md.research.elamur.ai/free/en/papers/deen-2026-extractive-summarization-arabic-documents-using/) adapts AraBERT to select key sentences from documents.

The third line is evaluation of large language models. [AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects](https://md.research.elamur.ai/free/en/papers/mustapha-2024-arastem-native-arabic-multiple-choice/) gathers questions written originally in Arabic, rather than translated, to test what models know in scientific and technical subjects. [Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora](https://md.research.elamur.ai/free/en/papers/abbas-2026-obscuring-data-contamination-through-translation/) asks whether test material can leak into training through translation. It proposes a detection method, TACD, that works without any training examples. These works fall under [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/). The contamination study also falls under [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/).

The fourth line concerns opinion, truthfulness and safety. [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/) moves beyond one overall polarity for each text. It attaches sentiment to particular targets across several topics and represents [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/).

[ARAFA: An LLM-Generated Arabic Fact-Checking Dataset](https://md.research.elamur.ai/free/en/papers/khalil-2026-arafa-llm-generated-arabic-fact/) responds to the modest size and narrow sources of earlier Arabic fact-checking collections. It builds a larger Modern Standard Arabic resource with the help of language models.

[Arabic Safety Alignment as Selective Refusal: An Empirical Study of SFT, DPO, and Guard Calibration](https://md.research.elamur.ai/free/en/papers/zbib-2026-arabic-safety-alignment-selective-refusal/) argues that a single refusal rate masks a tension between declining harmful requests and answering legitimate ones. It compares fine-tuning, preference optimization and guard calibration, and it carries the [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) topic.

The fifth line is speech. [Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study](https://md.research.elamur.ai/free/en/papers/baali-2023-unsupervised-data-selection-tts-using/) investigates whether a workable Arabic voice can be built from suitable segments of news broadcasts. This would remove the need to record a dedicated studio corpus. [AraSpot: Arabic Spoken Command Spotting](https://md.research.elamur.ai/free/en/papers/salhab-2023-araspot-arabic-spoken-command-spotting/) tackles keyword spotting. The task is to tell, from a recording lasting about a second, which command from a limited vocabulary was spoken. The topic of [Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) covers this work.

A related applied study is [Assessing Suicide Risk in Arabic Crisis Helpline Calls: A Comparison of Arabic and English Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2026-assessing-suicide-risk-arabic-crisis/). It asks whether risk can be estimated from helpline conversations without relying on sensitive audio, and it compares models built for Arabic with models built for English.

A final line deals with writing systems and errors. [AraSpell: A Deep Learning Approach for Arabic Spelling Correction](https://md.research.elamur.ai/free/en/papers/salhab-2024-araspell-deep-learning-approach-arabic/) replaces the older two-step routine of finding a mistake and then choosing a fix with an end-to-end neural approach. It falls under [Grammatical error correction & learner language](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/). [Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation](https://md.research.elamur.ai/free/en/papers/keleg-2026-romanized-arabic-across-dialects-views/) studies how Arabic is written in Latin letters across dialects. It builds a lexicon of how participants transliterate words, aligned character by character, along with a parallel collection matching Arabic-script text to its Romanized versions.

### Leading scientists

[Hazem Hajj](https://md.research.elamur.ai/free/en/authors/hazem-hajj/), at the American University of Beirut, comes first among the people connected with Lebanon. Hajj is a co-author of AraBERT with Wissam Antoun and Fady Baly, and also of the ArSentD-LEV sentiment corpus. Hajj's work here thus spans both resource building for Levantine social media and the pretraining of transformer models for Arabic.

[Ramy Baly](https://md.research.elamur.ai/free/en/authors/ramy-baly/), also at the American University of Beirut, is the first author of ArSentD-LEV, which annotates Levantine tweets with sentiment toward specific targets across a range of topics. Baly's work here sits within the sentiment and opinion line of the country's research.

[Chadi Helwe](https://md.research.elamur.ai/free/en/authors/chadi-helwe/) appears with the American University of Beirut and is also associated with the Lebanese American University on this site. Helwe is the one person here connected with both Beirut universities.

[Fadi Zaraket](https://md.research.elamur.ai/free/en/authors/fadi-zaraket/), at the American University of Beirut, wrote the 2012 morphological analyzer with Jad Makhlouta, the earliest Lebanese work here. Zaraket's contribution centres on modelling Arabic affixation through rules for joining morphemes. The approach is meant to avoid spelling out every combination of affixes in advance.

[Tymaa Hammouda](https://md.research.elamur.ai/free/en/authors/tymaa-hammouda/), at the American University of Beirut, is a co-author of the Nâbra corpus of Syrian Arabic with Amal Nayouf, [Mustafa Jarrar](https://md.research.elamur.ai/free/en/authors/mustafa-jarrar/) and others. Hammouda's work here belongs to the line of dialectal resources with detailed morphological annotation.

[Mahmoud Salhab](https://md.research.elamur.ai/free/en/authors/mahmoud-salhab/), at the Lebanese American University, is the first author of both AraSpell, written with Faisal Abu-Khzam, and AraSpot, written with Haidar Harmanani. Salhab's research here moves between written and spoken Arabic. One side is neural spelling correction and the other is the recognition of brief spoken commands.

[Mariette Awad](https://md.research.elamur.ai/free/en/authors/mariette-awad/), at the American University of Beirut, is the senior author on several recent works. These are the AraSTEM benchmark, the translation-based contamination study with Chaymaa Abbas and Nour Shamaa, and the SAraBERT summarization paper with Sami Shames El Deen. Awad's work here concentrates on how Arabic language models are adapted and how reliably they can be tested.

Beyond these names, [Khaled Shaban](https://md.research.elamur.ai/free/en/authors/khaled-shaban/) is also connected with the American University of Beirut on this site.

### Landmark works

AraBERT, from 2020, is the most cited Lebanese work in our library by a wide margin. It presented a transformer model pretrained specifically for Arabic. The motivation was the language's complex word structure and its distance from the languages on which earlier models were built. Its influence is visible within Lebanon's own output, since the 2026 SAraBERT paper takes it as its starting point.

ArSentD-LEV, from 2019, is the second most cited. It annotated Levantine tweets with sentiment tied to particular targets across multiple topics, instead of giving each text one coarse label. This gave finer-grained material for opinion analysis in a regional dialect.

Nâbra, from 2023, follows closely. It offers a word-level morphologically annotated corpus of Syrian Arabic, a dialect for which such material is scarce.

AraSTEM and AraSpell, both from 2024, come next. The first provides a native Arabic test of scientific knowledge for large language models. The second offers an end-to-end neural method for correcting Arabic spelling. The 2023 text-to-speech study and the 2012 morphological analyzer also draw citations. The 2026 papers have not yet gathered citations on this site.

### Labs and units

The two member institutions are the structure through which Lebanese research appears here. The American University of Beirut holds the larger share, together with most of the people connected with the country. The works associated with it are AraBERT, ArSentD-LEV, Nâbra, AraSTEM, the text-to-speech study, the 2012 analyzer, the contamination study and the SAraBERT paper. Its work covers nearly every topic in the Lebanese profile, from morphology to evaluation and summarization.

The Lebanese American University is represented by AraSpell, AraSpot and the 2026 study of Romanized Arabic. Its profile here leans toward the surface form of the language, meaning how it is spelled, spoken and transliterated, rather than toward pretraining or benchmarks. The remaining works tied to the country are the ARAFA fact-checking collection, the helpline study and the safety alignment paper. They belong to Lebanon's institutions, but no specific campus is indicated for them on this site.

### Collaborations

The works here show many co-authors around the Lebanese researchers. AraBERT brings together Wissam Antoun, Fady Baly and Hazem Hajj. ArSentD-LEV pairs Ramy Baly with Alaa Khaddaj, Hajj and others. Nâbra links Amal Nayouf, Tymaa Hammouda and Mustafa Jarrar with further co-authors. AraSTEM is a broad team effort, with Ahmad Mustapha, Hadi Al-Khansa, Hadi Al-Mubasher, Aya Mourad, Ranam Hamoud, Hasan El-Husseini and Marwah Al-Sakkaf working alongside Mariette Awad.

The text-to-speech paper connects Massa Baali with Tomoki Hayashi, Hamdy Mubarak and others. At the Lebanese American University, Mahmoud Salhab works with Faisal Abu-Khzam on spelling and with Haidar Harmanani on speech. The Romanized Arabic study involves Amr Keleg, Ahmed Amine Ben Abdallah, Taha Yassine and further authors.

Among the 2026 papers, ARAFA joins Christophe Khalil, Shady Elbassuoni and Rida Assaf. The helpline study involves Linhai Ma, Rita El Hachem, Mahatab El Hajj and others. The safety alignment work is by Mohamad Zbib and Ammar Mohanna.

Between the two Beirut universities, the clearest link on this site is Chadi Helwe, who is associated with both. Otherwise the two campuses appear to pursue their Arabic research along separate lines. The American University of Beirut concentrates on models and benchmarks, and the Lebanese American University on spelling, speech and script.

### Timeline

In 2012 the Lebanese works here begin with Zaraket and Makhlouta's rule-based morphological analyzer, the only work from that early period.

In 2019 and 2020 the research turns to large-scale resources and neural models. The ArSentD-LEV sentiment corpus of Levantine tweets came first, followed by AraBERT, the transformer model that became the most cited Lebanese work in our library.

In 2023 the range broadens to dialect morphology with Nâbra, to speech synthesis from broadcast news, and to spoken command spotting with AraSpot. AraSpot marks the Lebanese American University's first appearance among the works here.

In 2024 attention moves to large language models and writing quality, with the AraSTEM benchmark and the AraSpell spelling corrector.

In 2026 the largest group of papers appears, centred on the reliability and use of language models in Arabic. These works cover contamination through translation, summarization with SAraBERT, Romanized Arabic across dialects, fact-checking material generated with language models, suicide risk assessment from helpline calls, and safety alignment through selective refusal.

## Where

## Agglomerations (1)

1. [**Beirut**2 institutions*1,863 citations*](https://md.research.elamur.ai/free/en/agglomerations/beirut/)

## Institutions (2)

### 🎓 Universities (2)

1. **[American University of Beirut](https://md.research.elamur.ai/free/en/universities/american-university-of-beirut/)**University · [Beirut](https://md.research.elamur.ai/free/en/agglomerations/beirut/), Lebanon*17 works · 1,847 citations*
2. **[Lebanese American University](https://md.research.elamur.ai/free/en/universities/lebanese-american-university/)**University · [Beirut](https://md.research.elamur.ai/free/en/agglomerations/beirut/), Lebanon*3 works · 16 citations*

## People (10)

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

1. [**Hazem Hajj**American University of Beirut*6 papers here · 2,669 citations*](https://md.research.elamur.ai/free/en/authors/hazem-hajj/)
2. [**Wissam Antoun**American University of Beirut*3 papers here · 1,695 citations*](https://md.research.elamur.ai/free/en/authors/wissam-antoun/)
3. [**Fady Baly**American University of Beirut*3 papers here · 1,520 citations*](https://md.research.elamur.ai/free/en/authors/fady-baly/)
4. [**Ramy Baly**American University of Beirut*5 papers here · 1,475 citations*](https://md.research.elamur.ai/free/en/authors/ramy-baly/)
5. [**Chadi Helwe**American University of Beirut*2 papers here · 96 citations*](https://md.research.elamur.ai/free/en/authors/chadi-helwe/)
6. [**Fadi Zaraket**American University of Beirut*4 papers here · 81 citations*](https://md.research.elamur.ai/free/en/authors/fadi-zaraket/)
7. [**Tymaa Hammouda**American University of Beirut*4 papers here · 77 citations*](https://md.research.elamur.ai/free/en/authors/tymaa-hammouda/)
8. [**Mahmoud Salhab**Lebanese American University*2 papers here · 19 citations*](https://md.research.elamur.ai/free/en/authors/mahmoud-salhab/)
9. [**Mariette Awad**American University of Beirut*3 papers here · 17 citations*](https://md.research.elamur.ai/free/en/authors/mariette-awad/)
10. [**David Wilmsen**American University of Beirut*2 papers here · 5 citations*](https://md.research.elamur.ai/free/en/authors/david-wilmsen/)

## Works in our library (20)

Works of its institutions, the most cited first.

1. [**AraBERT: Transformer-based Model for Arabic Language Understanding arXiv:2003.00104**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,302 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
2. **[Neural Arabic Question Answering](https://md.research.elamur.ai/free/en/papers/mozannar-2019-neural-arabic-question-answering/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Hussein Mozannar, Elie Maamary, Karl El Hajal, et al.*2019 · 205 citations*
3. **[AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://md.research.elamur.ai/free/en/papers/antoun-2021-araelectra-pre-training-text-discriminators/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Wissam Antoun, Fady Baly, Hazem Hajj*2021 · 97 citations*
4. **[AraGPT2: Pre-Trained Transformer for Arabic Language Generation](https://md.research.elamur.ai/free/en/papers/antoun-2021-aragpt2-pre-trained-transformer-arabic/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Wissam Antoun, Fady Baly, Hazem Hajj*2021 · 79 citations*
5. [**ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets arXiv:1906.01830**Ramy Baly, Alaa Khaddaj, Hazem Hajj, et al.*2019 · 61 citations*](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/)
6. **[EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet](https://md.research.elamur.ai/free/en/papers/badaro-2018-emowordnet-automatic-expansion-emotion-lexicon/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Gilbert Badaro, Hussein Jundi, Hazem Hajj, et al.*2018 · 43 citations*
7. [**Nâbra: Syrian Arabic Dialects with Morphological Annotations**Amal Nayouf, Tymaa Hammouda, Mustafa Jarrar, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/nayouf-2023-nabra-syrian-arabic-dialects-morphological/)
8. [**AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects arXiv:2501.00559**Ahmad Mustapha; Hadi Al-Khansa; Hadi Al-Mubasher; Aya Mourad; Ranam Hamoud; Hasan El-Husseini; Marwah Al-Sakkaf;, Mariette Awad*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/mustapha-2024-arastem-native-arabic-multiple-choice/)
9. [**AraSpell: A Deep Learning Approach for Arabic Spelling Correction arXiv:2405.06981**Mahmoud Salhab, Faisal Abu-Khzam*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/salhab-2024-araspell-deep-learning-approach-arabic/)
10. [**Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study arXiv:2301.09099**Massa Baali, Tomoki Hayashi, Hamdy Mubarak, et al.*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/baali-2023-unsupervised-data-selection-tts-using/)
11. **[Regional standards and local routes in adoption techniques for specialised terminologies in the dialects of written Arabic](https://md.research.elamur.ai/free/en/papers/youssef-2009-regional-standards-local-routes-adoption/) [The Journal of Specialised Translation](https://md.research.elamur.ai/free/en/venues/the-journal-of-specialised-translation/)**Riham Osama Youssef, David Wilmsen*2009 · 5 citations*
12. [**Arabic Morphological Analyzer with Agglutinative Affix Morphemes and Fusional Concatenation Rules**Fadi Zaraket, Jad Makhlouta*2012 · 4 citations*](https://md.research.elamur.ai/free/en/papers/zaraket-2012-arabic-morphological-analyzer-agglutinative-affix/)
13. **[AraSpot: Arabic Spoken Command Spotting](https://md.research.elamur.ai/free/en/papers/salhab-2023-araspot-arabic-spoken-command-spotting/) [ACM Transactions on Asian and Low-Resource Language Information Processing](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-asian-and-low-resource-language-information-processing/)**Mahmoud Salhab, Haidar Harmanani*2023 · 1 citation*
14. [**Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora arXiv:2601.14994**Chaymaa Abbas, Nour Shamaa, Mariette Awad*2026*](https://md.research.elamur.ai/free/en/papers/abbas-2026-obscuring-data-contamination-through-translation/)
15. [**Extractive Summarization for Arabic Documents Using SAraBERT with a Semantic Siamese Similarity Evaluation Metric arXiv:2608.20964**Sami Shames El Deen, Mariette Awad*2026*](https://md.research.elamur.ai/free/en/papers/deen-2026-extractive-summarization-arabic-documents-using/)
16. [**QuranicMMLU: A Cognitively-Aware Benchmark for Evaluating Generative AI Solutions on Quranic Linguistic Knowledge arXiv:2609.22038 · Arabic NLP 2026**Rawan El Ghali; Umm Kulsoom; Anas Madkoor; Dima Faris Alsaudi; Roaa Abdelmagid; Roaa Ibrahim; Raghad Mousa; Hamza Aljaji; Abdullah Khanafer; Abdallah Alkanani; Salah Feras Alali; Rawan Khaled Mohamed;, Ehsaneddin Asgari*2026*](https://md.research.elamur.ai/free/en/papers/ghali-2026-quranicmmlu-cognitively-aware-benchmark-evaluating/)
17. [**Romanized Arabic Across Dialects: Views, Usage Patterns, and Linguistic Variation arXiv:2608.02555**Amr Keleg, Ahmed Amine Ben Abdallah, Taha Yassine, et al.*2026*](https://md.research.elamur.ai/free/en/papers/keleg-2026-romanized-arabic-across-dialects-views/)
18. [**ARAFA: An LLM-Generated Arabic Fact-Checking Dataset arXiv:2609.25833**Christophe Khalil, Shady Elbassuoni, Rida Assaf*2026*](https://md.research.elamur.ai/free/en/papers/khalil-2026-arafa-llm-generated-arabic-fact/)
19. [**Assessing Suicide Risk in Arabic Crisis Helpline Calls: A Comparison of Arabic and English Large Language Models arXiv:2609.00191**Linhai Ma, Rita El Hachem, Mahatab El Hajj, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ma-2026-assessing-suicide-risk-arabic-crisis/)
20. [**Arabic Safety Alignment as Selective Refusal: An Empirical Study of SFT, DPO, and Guard Calibration arXiv:2608.29378**Mohamad Zbib, Ammar Mohanna*2026*](https://md.research.elamur.ai/free/en/papers/zbib-2026-arabic-safety-alignment-selective-refusal/)

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 16 The field of science this work belongs to (OpenAlex fields).[Computer Science · 19 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 18 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 19 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 18 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Psychology · 4 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Experimental and Cognitive Psychology · 3 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Signal Processing · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) · [Social Sciences · 3 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Clinical Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/clinical-psychology/) · [Education · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/education/) · [Human-Computer Interaction · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/human-computer-interaction/) · [Safety Research · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Social Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/social-psychology/)+1 more

🧭 Topics 13 Research subjects and languages the work is about.[Semitic languages · 19 works](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Multilingual & cross-lingual NLP · 1 work](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Sentiment & emotion · 2 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Text generation · 1 work](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Morphology · 2 works](https://md.research.elamur.ai/free/en/topics/morphology/) · [English · 2 works](https://md.research.elamur.ai/free/en/topics/english/) · [Grammatical error correction & learner language · 1 work](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · [Speech & spoken language · 1 work](https://md.research.elamur.ai/free/en/topics/speech/) · [Evaluation & benchmarks · 3 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Summarization · 1 work](https://md.research.elamur.ai/free/en/topics/summarization/)

💡 Concepts 3 Key ideas and methods explained on our concept pages.[Morphology · 1 work](https://md.research.elamur.ai/free/en/concepts/morphology/) · [Natural language understanding · 1 work](https://md.research.elamur.ai/free/en/concepts/natural-language-understanding/) · [Transformer and attention · 1 work](https://md.research.elamur.ai/free/en/concepts/transformer/)

## Works by year

### 2020s

[**2026** 7 works Works of Lebanon from 2026](https://md.research.elamur.ai/free/en/countries/lebanon/year/2026/) · [**2024** 2 works Works of Lebanon from 2024](https://md.research.elamur.ai/free/en/countries/lebanon/year/2024/) · [**2023** 3 works Works of Lebanon from 2023](https://md.research.elamur.ai/free/en/countries/lebanon/year/2023/) · [**2021** 2 works Works of Lebanon from 2021](https://md.research.elamur.ai/free/en/countries/lebanon/year/2021/) · [**2020** 1 work Works of Lebanon from 2020](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)

### 2010s

[**2019** 2 works Works of Lebanon from 2019](https://md.research.elamur.ai/free/en/countries/lebanon/year/2019/) · [**2018** 1 work Works of Lebanon from 2018](https://md.research.elamur.ai/free/en/papers/badaro-2018-emowordnet-automatic-expansion-emotion-lexicon/) · [**2012** 1 work Works of Lebanon from 2012](https://md.research.elamur.ai/free/en/papers/zaraket-2012-arabic-morphological-analyzer-agglutinative-affix/)

### 2000s

[**2009** 1 work Works of Lebanon from 2009](https://md.research.elamur.ai/free/en/papers/youssef-2009-regional-standards-local-routes-adoption/)

- [← back to](https://md.research.elamur.ai/free/en/countries/) — countries

Updated 24 October 2026