---
title: "Who cites Pedro Ortiz Suarez"
description: "1,124 papers of the Elamur Research library cite the works of Pedro Ortiz Suarez, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/pedro-ortiz-suarez/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/pedro-ortiz-suarez/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Pedro Ortiz Suarez](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/) / cited by

# Who cites Pedro Ortiz Suarez

1,274 citations of their works from 1,124 papers of our library (of the 140,884 whose bibliographies we read) · 126 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Pedro Ortiz Suarez](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/)

**1** [2](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/cited-by/page/2/)

1. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
2. [**Pre-trained Models for Natural Language Processing: A Survey**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,740 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
3. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
4. [**mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 1,574 citations*](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/)
5. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
6. [**AraBERT: Transformer-based Model for Arabic Language Understanding**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,297 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
7. [**Big Bird: Transformers for Longer Sequences**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 992 citations*](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/)
8. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
9. [**GPT-NeoX-20B: An Open-Source Autoregressive Language Model**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 818 citations*](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/)
10. [**PhoBERT: Pre-trained language models for Vietnamese**Dat Quoc Nguyen, Anh Tuan Nguyen*2020 · 571 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2020-phobert-pre-trained-language-models/)
11. [**ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic**Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi*2021 · 478 citations*](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/)
12. [**Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets**Julia Kreutzer, Isaac Caswell, Lisa Wang, et al.*2021 · 447 citations*](https://md.research.elamur.ai/free/en/papers/kreutzer-2021-quality-glance-audit-web-crawled/)
13. [**How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models**Phillip Rust, Jonas Pfeiffer, Ivan Vulić, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/)
14. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
15. [**Ethical and social risks of harm from Language Models**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 421 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
16. [**KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media**Ali Safaya, Moutasem Abdullatif, Deniz Yuret*2020 · 409 citations*](https://md.research.elamur.ai/free/en/papers/safaya-2020-kuisail-semeval-2020-task-12/)
17. [**The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 404 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/)
18. [**German's Next Language Model**Branden Chan, Stefan Schweter, Timo Möller*2020 · 385 citations*](https://md.research.elamur.ai/free/en/papers/chan-2020-german-s-next-language-model/)
19. [**The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models**Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, et al.*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/)
20. [**Datasets: A Community Library for Natural Language Processing**Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, et al.*2021 · 358 citations*](https://md.research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/)
21. [**Large Language Models Struggle to Learn Long-Tail Knowledge**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 352 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/)
22. [**RobBERT: a Dutch RoBERTa-based Language Model**Pieter Delobelle, Thomas Winters, Bettina Berendt*2020 · 346 citations*](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/)
23. [**Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages**Gowtham Ramesh, Sumanth Doddapaneni, Aravinth Bheemaraj, et al.*2022 · 307 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2022-samanantar-largest-publicly-available-parallel/)
24. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
25. [**BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, et al.*2022 · 296 citations*](https://md.research.elamur.ai/free/en/papers/bhattacharjee-2022-banglabert-language-model-pretraining-benchmarks/)
26. [**IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding**Bryan Wilie, Karissa Vincentio, Genta Indra Winata, et al.*2020 · 265 citations*](https://md.research.elamur.ai/free/en/papers/wilie-2020-indonlu-benchmark-resources-evaluating-indonesian/)
27. [**The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants**Lucas Bandarkar, Davis Liang, Benjamin Muller, et al.*2023 · 259 citations*](https://md.research.elamur.ai/free/en/papers/bandarkar-2023-belebele-benchmark-parallel-reading-comprehension/)
28. [**Multilingual is not enough: BERT for Finnish**Antti Virtanen, Jenna Kanerva, Rami Ilo, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-multilingual-enough-bert-finnish/)
29. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 245 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
30. [**NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark**Oscar Sainz, Jon Campos, Iker García-Ferrero, et al.*2023 · 245 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/)
31. [**In-context Examples Selection for Machine Translation**Sweta Agrawal, Chunting Zhou, Mike Lewis, et al.*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/)
32. [**A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages**Pedro Javier Ortiz Suárez, Laurent Romary, Benoît Sagot*2020 · 219 citations*](https://md.research.elamur.ai/free/en/papers/suarez-2020-monolingual-approach-contextualized-word-embeddings/)
33. [**MADLAD-400: A Multilingual And Document-Level Large Audited Dataset**Sneha Kudugunta, Isaac Caswell, Biao Zhang, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/)
34. [**mGPT: Few-Shot Learners Go Multilingual**Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, et al.*2022 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shliazhko-2022-mgpt-few-shot-learners-go/)
35. [**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/)
36. [**AraT5: Text-to-Text Transformers for Arabic Language Generation**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed*2022 · 201 citations*](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/)
37. [**IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages**Jay Gala, Pranjal A. Chitale, Raghavan AK, et al.*2023 · 200 citations*](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/)
38. [**What matters when building vision-language models?**Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2024-matters-building-vision-language-models/)
39. [**HONEST: Measuring Hurtful Sentence Completion in Language Models**Debora Nozza, Federico Bianchi, Dirk Hovy*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/nozza-2021-honest-measuring-hurtful-sentence-completion/)
40. [**When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models**Benjamin Muller, Antonios Anastasopoulos, Benoît Sagot, et al.*2021 · 189 citations*](https://md.research.elamur.ai/free/en/papers/muller-2021-when-being-unseen-mbert-is/)
41. [**OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents**Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-obelics-open-web-scale-filtered/)
42. [**Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/)
43. [**CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/)
44. [**Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages**Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/doddapaneni-2023-leaving-no-indic-language-behind/)
45. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/)
46. [**MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer**Ilias Chalkidis, Manos Fergadiotis, Ion Androutsopoulos*2021 · 160 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-multieurlex-multi-lingual-multi-label/)
47. [**DataComp-LM: In search of the next generation of training sets for language models**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 156 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/)
48. [**A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation**David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, et al.*2022 · 155 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/)
49. [**Towards a Cleaner Document-Oriented Multilingual Crawled Corpus**Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, et al.*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/abadji-2022-towards-cleaner-document-oriented-multilingual/)
50. [**ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages**Nathaniel Robinson, Perez Ogayo, David R. Mortensen, et al.*2023 · 151 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2023-chatgpt-mt-competitive-high-but/)
51. [**Pre-Training BERT on Arabic Tweets: Practical Considerations**Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, et al.*2021 · 150 citations*](https://md.research.elamur.ai/free/en/papers/abdelali-2021-pre-training-bert-arabic-tweets/)
52. [**Language Model Tokenizers Introduce Unfairness Between Languages**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/)
53. [**Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation**Nuno M. Guerreiro, Elena Voita, André Martins*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-looking-needle-haystack-comprehensive-study/)
54. [**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/)
55. [**Extracting Latent Steering Vectors from Pretrained Language Models**Nishant Subramani, Nivedita Suresh, Matthew Peters*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/)
56. [**Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark**Joel Niklaus, Ilias Chalkidis, Matthias Stürmer*2021 · 129 citations*](https://md.research.elamur.ai/free/en/papers/niklaus-2021-swiss-judgment-prediction-multilingual-legal/)
57. [**LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain**Joel Niklaus, Veton Matoshi, Pooja Rani, et al.*2023 · 122 citations*](https://md.research.elamur.ai/free/en/papers/niklaus-2023-lextreme-multi-lingual-multi-task/)
58. [**GlotLID: Language Identification for Low-Resource Languages**Amir Hossein Kargaran, Ayyoob Imani, François Yvon, et al.*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/)
59. [**Playing with Words at the National Library of Sweden — Making a Swedish BERT**Martin Malmsten, Love Borjeson, Chris Haffenden*2020 · 105 citations*](https://md.research.elamur.ai/free/en/papers/malmsten-2020-playing-words-national-library-sweden/)
60. [**Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words**Valentin, Hofmann*2021 · 101 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2021-superbizarre-derivational/)
61. [**WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models**Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz*2022 · 90 citations*](https://md.research.elamur.ai/free/en/papers/minixhofer-2022-wechsel-effective-initialization-subword-embeddings/)
62. [**HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition**Avihay Chriqui, Inbal Yahav*2021 · 68 citations*](https://md.research.elamur.ai/free/en/papers/chriqui-2021-hebert-hebemo-hebrew-bert-model/)
63. [**Tokenization Is More Than Compression**Craig W Schmidt, Varshini Reddy, Haoran Zhang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/schmidt-2024-tokenization-is-more-than-compression/)
64. [**An Open Dataset and Model for Language Identification**Laurie Burchell, Alexandra Birch, Nikolay Bogoychev, et al.*2023 · 59 citations*](https://md.research.elamur.ai/free/en/papers/burchell-2023-open-dataset-model-language-identification/)
65. [**AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level**Amit Seker, Elron Bandel, Dan Bareket, et al.*2022 · 57 citations*](https://md.research.elamur.ai/free/en/papers/seker-2022-alephbert-language-model-pre-training/)
66. [**AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With**Amit Seker, Elron Bandel, Dan Bareket, et al.*2021 · 56 citations*](https://md.research.elamur.ai/free/en/papers/seker-2021-alephbert-hebrew-large-pre-trained/)
67. [**DziriBERT: a Pre-trained Language Model for the Algerian Dialect**Amine Abdaoui, Mohamed Berrimi, Mourad Oussalah, et al.*2021 · 38 citations*](https://md.research.elamur.ai/free/en/papers/abdaoui-2021-dziribert-pre-trained-language-model/)
68. [**TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages**Anirudh Srinivasan, Eunsol Choi*2022 · 29 citations*](https://md.research.elamur.ai/free/en/papers/srinivasan-2022-tydip-dataset-politeness-classification-nine/)
69. [**Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi**Benjamin Muller, Benoit Sagot, Djame Seddah*2020 · 26 citations*](https://md.research.elamur.ai/free/en/papers/muller-2020-multilingual-language-models-transfer-unseen/)
70. [**An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)**Laurie Burchell, Ona de Gibert, Nikolay Arefyev, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/burchell-2025-expanded-massive-multilingual-dataset-high/)
71. [**A Survey of Corpora for Germanic Low-Resource Languages and Dialects**Verena Blaschke, Hinrich Schuetze, Barbara Plank*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/blaschke-2023-survey-corpora-germanic-low-resource/)
72. [**Multilingual Irony Detection with Dependency Syntax and Neural Models**Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, et al.*2020 · 21 citations*](https://md.research.elamur.ai/free/en/papers/cignarella-2020-multilingual-irony-detection-dependency-syntax/)
73. [**Hybrid Enhanced Universal Dependencies Parsing**Johannes Heinecke*2020 · 20 citations*](https://md.research.elamur.ai/free/en/papers/heinecke-2020-hybrid-enhanced-universal-dependencies-parsing/)
74. [**A Large and Diverse Arabic Corpus for Language Modeling**Abbas Raza Ali, Muhammad Ajmal Siddiqui, Rema Algunaibet, et al.*2022 · 16 citations*](https://md.research.elamur.ai/free/en/papers/ali-2022-large-diverse-arabic-corpus-language/)
75. [**JABER and SABER: Junior and Senior Arabic BERt**Abbas Ghaddar, Yimeng Wu, Ahmad Rashid, et al.*2021 · 16 citations*](https://md.research.elamur.ai/free/en/papers/ghaddar-2021-jaber-saber-junior-senior-arabic/)
76. [**HeRo: RoBERTa and Longformer Hebrew Language Models**Vitaly Shalumov, Harel Haskey*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shalumov-2023-hero-roberta-longformer-hebrew-language/)
77. [**Alignment at Pre-training! Towards Native Alignment for Arabic LLMs**Juhao Liang, Zhenyang Cai, Jianqing Zhu, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/liang-2024-alignment-pre-training-towards-native/)
78. [**ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution**Milan Straka, Jana Straková*2022 · 12 citations*](https://md.research.elamur.ai/free/en/papers/straka-2022-fal-corpipe-crac-2022-effectivity/)
79. [**ParaShoot: A Hebrew Question Answering Dataset**Omri Keren, Omer Levy*2021 · 12 citations*](https://md.research.elamur.ai/free/en/papers/keren-2021-parashoot-hebrew-question-answering-dataset/)
80. [**Neural Coreference Resolution for Arabic**Abdulrahman Aloraini, Juntao Yu, Massimo Poesio*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/aloraini-2020-neural-coreference-resolution-arabic/)
81. [**Multilingual Coreference Resolution in Multiparty Dialogue**Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-multilingual-coreference-resolution-multiparty-dialogue/)
82. [**On the Importance of Tokenization in Arabic Embedding Models**Mohamed Alkaoud, Mairaj Syed*2020 · 11 citations*](https://md.research.elamur.ai/free/en/papers/alkaoud-2020-importance-tokenization-arabic-embedding-models/)
83. [**Transformer-based Hebrew NLP models for Short Answer Scoring in Biology**Abigail Gurin Schleifer, Beata Beigman Klebanov, Moriah Ariely, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/schleifer-2023-transformer-based-hebrew-nlp-models/)
84. [**Multilingual Sequence-to-Sequence Models for Hebrew NLP**Matan Eyal, Hila Noga, Roee Aharoni, et al.*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/eyal-2023-multilingual-sequence-sequence-models-hebrew/)
85. [**Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Understanding**Abbas Ghaddar, Yimeng Wu, Sunyam Bagga, et al.*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/ghaddar-2022-revisiting-pre-trained-language-models-their/)
86. [**The ADAPT Enhanced Dependency Parser at the IWPT 2020 Shared Task**James Barry, Joachim Wagner, Jennifer Foster*2020 · 9 citations*](https://md.research.elamur.ai/free/en/papers/barry-2020-adapt-enhanced-dependency-parser-iwpt/)
87. [**SemEval-2022 Task 3: PreTENS — Evaluating Neural Networks on Presuppositional Semantic Knowledge**Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, et al.*2022 · 8 citations*](https://md.research.elamur.ai/free/en/papers/zamparelli-2022-semeval-2022-task-3-pretens/)
88. [**TArC: Tunisian Arabish Corpus, First complete release**Elisa Gugliotta, Marco Dinarelli*2022 · 8 citations*](https://md.research.elamur.ai/free/en/papers/gugliotta-2022-tarc-tunisian-arabish-corpus-first/)
89. [**Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning**Yang Zhang, Amr Mohamed, Hadi Abdine, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-random-sampling-efficient-language/)
90. [**Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic**Zaid Alyafeai, Michael Pieler, Hannah Teufel, et al.*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2024-arabic-stable-lm-adapting-stable/)
91. [**Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models**Mohamed Taher Alrefaie, Nour Eldin Morsy, Nada Samir*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/alrefaie-2024-exploring-tokenization-strategies-vocabulary-sizes/)
92. [**Kuwain 1.5B: An Arabic SLM via Language Injection**Khalil Hennara, Sara Chrouf, Mohamed Motaism Hamed, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/hennara-2025-kuwain-1-5b-arabic-slm/)
93. [**MoVoC: Morphology-Aware Subword Construction for Ge'ez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction/)
94. [**MoVoC: Morphology-Aware Subword Construction for Geez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction-geez/)
95. [**MultiCoPIE: A Multilingual Corpus of Potentially Idiomatic Expressions for Cross-lingual PIE Disambiguation**Uliana Sentsova, Debora Ciminari, Josef van Genabith, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/sentsova-2025-multicopie-multilingual-corpus-potentially-idiomatic/)
96. [**Do Diacritics Matter? Evaluating the Impact of Arabic Diacritics on Tokenization and LLM Benchmarks**Go Inoue, Bashar Alhafni, Nizar Habash, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/inoue-2026-do-diacritics-matter-evaluating-impact/)
97. [**Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model**Ahlam Alrehili, Areej Alhothali*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/alrehili-2025-towards-development-balanced-synthetic-data/)
98. [**Evaluating large language models for the tasks of PoS tagging within the Universal Dependency framework**Mateus Machado, Evandro Ruiz*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/machado-2024-evaluating-large-language-models-tasks/)
99. [**Out-of-Domain Dependency Parsing for Dialects of Arabic: A Case Study**Noor Abo Mokh, Daniel Dakota, Sandra Kübler*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/mokh-2024-out-domain-dependency-parsing-dialects/)
100. [**Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens**Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nigatu-2026-evaluating-machine-translation-datasets-low/)

**1** [2](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/authors/pedro-ortiz-suarez/) — Pedro Ortiz Suarez