---
title: "Who cites Guillaume Wenzek"
description: "2,426 papers of the Elamur Research library cite the works of Guillaume Wenzek, found in their bibliographies · page 1 of 3 · 1–100; the most cited first."
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---
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# Who cites Guillaume Wenzek

2,996 citations of their works from 2,426 papers of our library (of the 140,884 whose bibliographies we read) · 243 with a page here, the most cited first · page 1 of 3 · 1–100 · [← Guillaume Wenzek](https://md.research.elamur.ai/free/en/authors/guillaume-wenzek/)

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1. [**LLaMA: Open and Efficient Foundation Language Models**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 5,609 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
2. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
3. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
4. [**Toolformer: Language Models Can Teach Themselves to Use Tools**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,020 citations*](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/)
5. [**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/)
6. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
7. [**DINOv2: Learning Robust Visual Features without Supervision**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,425 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
8. [**Language-agnostic BERT Sentence Embedding**Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, et al.*2020 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/)
9. [**CamemBERT: a Tasty French Language Model**Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.*2019 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/)
10. [**M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation**Jianlv Chen, Shitao Xiao, Peitian Zhang, et al.*2024 · 1,196 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-m3-embedding-multi-linguality/)
11. [**A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly**Yifan Yao, Jinhao Duan, Kaidi Xu, et al.*2023 · 1,181 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/)
12. [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 866 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
13. [**MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer**Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, et al.*2020 · 831 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-mad-x-adapter-based-framework/)
14. [**Unsupervised Dense Information Retrieval with Contrastive Learning**Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.*2021 · 824 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/)
15. [**XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale**Arun Babu, Changhan Wang, Andros Tjandra, et al.*2021 · 799 citations*](https://md.research.elamur.ai/free/en/papers/babu-2021-xls-r-self-supervised-cross/)
16. [**IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
17. [**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/)
18. [**Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine**Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, et al.*2023 · 626 citations*](https://md.research.elamur.ai/free/en/papers/jiao-2023-chatgpt-good-translator-yes-gpt/)
19. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
20. [**Baichuan 2: Open Large-scale Language Models**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 512 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
21. [**Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis**Wenhao Zhu, Hongyi Liu, Qingxiu Dong, et al.*2024 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/)
22. [**Few-shot Learning with Multilingual Language Models**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
23. [**From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers**Anne Lauscher, Vinit Ravishankar, Ivan Vulić, et al.*2020 · 461 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2020-zero-hero-limitations-zero-shot/)
24. [**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/)
25. [**The Faiss library**Matthijs Douze, Alexandr Guzhva, Chengqi Deng, et al.*2024 · 442 citations*](https://md.research.elamur.ai/free/en/papers/douze-2024-faiss-library/)
26. [**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/)
27. [**Benchmarking Retrieval-Augmented Generation for Medicine**Guangzhi Xiong, Qiao Jin, Zhiyong Lu, et al.*2024 · 426 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/)
28. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
29. [**FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech**Alexis Conneau, Min Ma, Simran Khanuja, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2022-fleurs-few-shot-learning-evaluation/)
30. [**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/)
31. [**Towards General Text Embeddings with Multi-stage Contrastive Learning**Zehan Li, Xin Zhang, Yanzhao Zhang, et al.*2023 · 378 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-towards-general-text-embeddings-multi/)
32. [**Tool Learning with Foundation Models**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 376 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/)
33. [**XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation**Yaobo Liang, Nan Duan, Yeyun Gong, et al.*2020 · 356 citations*](https://md.research.elamur.ai/free/en/papers/liang-2020-xglue-new-benchmark-dataset-cross/)
34. [**CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB**Holger Schwenk, Guillaume Wenzek, Sergey Edunov, et al.*2019 · 354 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2019-ccmatrix-mining-billions-high-quality/)
35. [**Internet-Augmented Dialogue Generation**Mojtaba Komeili, Kurt Shuster, Jason Weston*2022 · 349 citations*](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/)
36. [**Multilingual E5 Text Embeddings: A Technical Report**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 343 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-multilingual-e5-text-embeddings-technical/)
37. [**Scaling Speech Technology to 1,000+ Languages**Vineel Pratap, Andros Tjandra, Bowen Shi, et al.*2023 · 331 citations*](https://md.research.elamur.ai/free/en/papers/pratap-2023-scaling-speech-technology-1-000/)
38. [**MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities**Weihao Yu, Zhengyuan Yang, Linjie Li, et al.*2023 · 328 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/)
39. [**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/)
40. [**InternLM2 Technical Report**Zheng Cai, Maosong Cao, Haojiong Chen, et al.*2024 · 299 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-internlm2-technical-report/)
41. [**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/)
42. [**Prompting Large Language Model for Machine Translation: A Case Study**Biao Zhang, Barry Haddow, Alexandra Birch*2023 · 293 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-prompting-large-language-model-machine/)
43. [**Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust**Markus Freitag, Ricardo Rei, Nitika Mathur, et al.*2022 · 263 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2022-results-wmt22-metrics-shared-task/)
44. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
45. [**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/)
46. [**Survey of Low-Resource Machine Translation**Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, et al.*2022 · 251 citations*](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/)
47. [**RedPajama: an Open Dataset for Training Large Language Models**Maurice Weber, Daniel Fu, Quentin Anthony, et al.*2024 · 247 citations*](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/)
48. [**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/)
49. [**Hallucinations in Large Multilingual Translation Models**Nuno M. Guerreiro, Duarte M. Alves, Jonas Waldendorf, et al.*2023 · 244 citations*](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-hallucinations-large-multilingual-translation-models/)
50. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 243 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/)
51. [**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/)
52. [**Tower: An Open Multilingual Large Language Model for Translation-Related Tasks**Duarte M. Alves, Jose Pombal, Nuno M. Guerreiro, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/alves-2024-tower-open-multilingual-large-language/)
53. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
54. [**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/)
55. [**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/)
56. [**Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation**Haoran Xu, Amr Sharaf, Yunmo Chen, et al.*2024 · 207 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/)
57. [**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/)
58. [**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/)
59. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
60. [**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/)
61. [**Towards Making the Most of ChatGPT for Machine Translation**Keqin Peng, Liang Ding, Qihuang Zhong, et al.*2023 · 190 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-making-most-chatgpt-machine-translation/)
62. [**Findings of the 2021 Conference on Machine Translation (WMT21)**Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, et al.*2021 · 189 citations*](https://md.research.elamur.ai/free/en/papers/akhbardeh-2021-findings-2021-conference-machine-translation/)
63. [**Adaptive Machine Translation with Large Language Models**Yasmin Moslem, Rejwanul Haque, John D. Kelleher, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/moslem-2023-adaptive-machine-translation-large-language/)
64. [**SeamlessM4T: Massively Multilingual & Multimodal Machine Translation**Seamless Communication, Loic Barrault, Yu-An Chung, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/communication-2023-seamlessm4t-massively-multilingual-multimodal-machine/)
65. [**Pre-training via Paraphrasing**Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, et al.*2020 · 181 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-pre-training-paraphrasing/)
66. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 180 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/)
67. [**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/)
68. [**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/)
69. [**8-bit Optimizers via Block-wise Quantization**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
70. [**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/)
71. [**Aya 23: Open Weight Releases to Further Multilingual Progress**Viraat Aryabumi, John Dang, Dwarak Talupuru, et al.*2024 · 171 citations*](https://md.research.elamur.ai/free/en/papers/aryabumi-2024-aya-23-open-weight-releases/)
72. [**Seamless: Multilingual Expressive and Streaming Speech Translation**Seamless Communication, Loic Barrault, Yu-An Chung, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/communication-2023-seamless-multilingual-expressive-streaming-speech/)
73. [**Low-Resource Languages Jailbreak GPT-4**Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/)
74. [**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/)
75. [**Larger-Scale Transformers for Multilingual Masked Language Modeling**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 160 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/)
76. [**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/)
77. [**Results of WMT23 Metrics Shared Task: Metrics Might Be Guilty but References Are Not Innocent**Markus Freitag, Nitika Mathur, Chi-kiu Lo, et al.*2023 · 155 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2023-results-wmt23-metrics-shared-task/)
78. [**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/)
79. [**FINDINGS OF THE IWSLT 2023 EVALUATION CAMPAIGN**Milind Agarwal, Sweta Agrawal, Antonios Anastasopoulos, et al.*2023 · 153 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-findings-iwslt-2023-evaluation-campaign/)
80. [**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/)
81. [**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/)
82. [**MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages**Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, et al.*2023 · 148 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-miracl-multilingual-retrieval-dataset-covering/)
83. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 145 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
84. [**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/)
85. [**SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects**David Ifeoluwa Adelani, Hannah Liu, Xiaoyu Shen, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2023-sib-200-simple-inclusive-big/)
86. [**Many-Shot In-Context Learning**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 134 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/)
87. [**EuroLLM: Multilingual Language Models for Europe**Pedro Henrique Martins, Patrick Fernandes, Joao Alves, et al.*2024 · 131 citations*](https://md.research.elamur.ai/free/en/papers/martins-2024-eurollm-multilingual-language-models-europe/)
88. [**Findings of the WMT 2023 Shared Task on Quality Estimation**Frederic Blain, Chrysoula Zerva, Ricardo Rei, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/blain-2023-findings-wmt-2023-shared-task/)
89. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
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