---
title: "Who cites Revisiting Pre-trained Language — 9 citing papers"
description: "9 papers of the Elamur Research library cite «Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language…"
canonical: "https://research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Revisiting Pre-trained Language Models and their…](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models/) / cited by

# Who cites «Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing»

9 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing»](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models/)

1. **[AlGhafa Evaluation Benchmark for Arabic Language Models](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-alghafa-evaluation-benchmark-arabic-language/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Ebtesam Almazrouei, Ruxandra Cojocaru, Michele Baldo, et al.*2023 · 46 citations*
2. **[Strategies for Arabic Readability Modeling](https://md.research.elamur.ai/free/en/papers/liberato-2024-strategies-arabic-readability-modeling/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Juan Liberato, Bashar Alhafni, Muhamed Khalil, et al.*2024 · 26 citations*
3. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models arXiv:2306.16322**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/)
4. **[A Survey of Large Language Models for Arabic Language and its Dialects](https://md.research.elamur.ai/free/en/papers/mashaabi-2024-survey-large-language-models-arabic/) [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/)**Malak Mashaabi, Shahad Al-Khalifa, Hend Al-Khalifa*2024 · 24 citations*
5. [**ArabianGPT: Native Arabic GPT-based Large Language Model arXiv:2402.15313**Anis Koubaa, Adel Ammar, Lahouari Ghouti, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/koubaa-2024-arabiangpt-native-arabic-gpt-based/)
6. **[Arabic dialect identification: An in-depth error analysis on the MADAR parallel corpus](https://md.research.elamur.ai/free/en/papers/olsen-2023-arabic-dialect-identification-depth-error/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Helene Olsen, Samia Touileb, Erik Velldal*2023 · 8 citations*
7. **[Alex-U 2023 NLP at WojoodNER shared task: AraBINDER (Bi-encoder for Arabic Named Entity Recognition)](https://md.research.elamur.ai/free/en/papers/hussein-2023-alex-u-2023-nlp-wojoodner/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Mariam Hussein, Sarah Khaled, Marwan Torki, et al.*2023 · 5 citations*
8. **[NeoAraBERT: A Modern Foundation Model for Arabic Embeddings with Diacritics-Aware Tokenization and POS-Targeted Masking](https://md.research.elamur.ai/free/en/papers/chakra-2026-neoarabert-modern-foundation-model-arabic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chadi Abou Chakra, Hadi Khaled Hamoud, Osama Rakan Al Mraikhat, et al.*2026 · 1 citation*

1 more paper of our library cites it; it has no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models/) — Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing