---
title: "Who cites Introduction to the CoNLL-2003 Shared Task…"
description: "1,479 papers of the Elamur Research library cite «Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition», found in…"
canonical: "https://research.elamur.ai/free/en/papers/sang-2003-introduction-conll-2003-shared-task/cited-by/"
---
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# Who cites «Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition»

1,479 papers of our library cite it (of the 140,884 whose bibliographies we read) · 114 with a page here, the most cited first · page 1 of 2 · 1–100 · [← the paper](https://md.research.elamur.ai/free/en/papers/sang-2003-introduction-conll-2003-shared-task/)

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2. [**GloVe: Global Vectors for Word Representation**Jeffrey Pennington, Richard Socher, Christopher Manning*2014 · 5,962 citations*](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)
3. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
4. [**Deep Contextualized Word Representations**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 3,240 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/)
5. [**Stanza: A Python Natural Language Processing Toolkit for Many Human Languages**Peng Qi, Yuhao Zhang, Yuhui Zhang, et al.*2020 · 2,347 citations*](https://md.research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/)
6. [**Named Entity Recognition with Bidirectional LSTM-CNNs**Jason P. C. Chiu, Eric Nichols*2015 · 2,276 citations*](https://md.research.elamur.ai/free/en/papers/chiu-2015-named-entity-recognition-bidirectional-lstm/)
7. [**Design Challenges and Misconceptions in Named Entity Recognition**Lev Ratinov, Dan Roth*2009 · 1,836 citations*](https://md.research.elamur.ai/free/en/papers/ratinov-2009-design-challenges-misconceptions-named-entity/)
8. [**Contextual String Embeddings for Sequence Labeling**Alan Akbik, Duncan Blythe, Roland Vollgraf*2018 · 1,390 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/)
9. [**brat: a Web-based Tool for NLP-Assisted Text Annotation**Pontus Stenetorp, Sampo Pyysalo, Goran Topić, et al.*2012 · 1,368 citations*](https://md.research.elamur.ai/free/en/papers/stenetorp-2012-brat-web-based-tool-nlp/)
10. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,316 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
11. [**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/)
12. [**PIQA: Reasoning about Physical Commonsense in Natural Language**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,218 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/)
13. [**MASS: Masked Sequence to Sequence Pre-training for Language Generation**Kaitao Song, Xu Tan, Tao Qin, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-mass-masked-sequence-sequence-pre/)
14. [**Neural Architectures for Named Entity Recognition**Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, et al.*2016 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/lample-2016-neural-architectures-named-entity-recognition/)
15. [**P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2022 · 1,013 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-p-tuning-prompt-tuning-can/)
16. [**Linguistic Knowledge and Transferability of Contextual Representations**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,011 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/)
17. [**Dependency Tree Kernels for Relation Extraction**Aron Culotta, Jeffrey Sorensen*2004 · 998 citations*](https://md.research.elamur.ai/free/en/papers/culotta-2004-dependency-tree-kernels-relation-extraction/)
18. [**ERNIE 2.0: A Continual Pre-training Framework for Language Understanding**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 976 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-2-0-continual/)
19. [**Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling**Xavier Carreras, Lluís Màrquez*2005 · 899 citations*](https://md.research.elamur.ai/free/en/papers/carreras-2005-introduction-conll-2005-shared-task/)
20. [**End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF**Xuezhe Ma, Eduard Hovy*2016 · 852 citations*](https://md.research.elamur.ai/free/en/papers/ma-2016-end-end-sequence-labeling-bi/)
21. [**A Unified MRC Framework for Named Entity Recognition**Xiaoya Li, Jingrong Feng, Yuxian Meng, et al.*2020 · 833 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-unified-mrc-framework-named-entity/)
22. [**Is ChatGPT a General-Purpose Natural Language Processing Task Solver?**Chengwei Qin, Aston Zhang, Zhuosheng Zhang, et al.*2023 · 818 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-chatgpt-general-purpose-natural-language/)
23. [**Entity, Relation, and Event Extraction with Contextualized Span Representations**David Wadden, Ulme Wennberg, Yi Luan, et al.*2019 · 818 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/)
24. [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
25. [**Semi-supervised sequence tagging with bidirectional language models**Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, et al.*2017 · 786 citations*](https://md.research.elamur.ai/free/en/papers/peters-2017-semi-supervised-sequence-tagging-bidirectional/)
26. [**GLM-130B: An Open Bilingual Pre-trained Model**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 775 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
27. [**Dice Loss for Data-imbalanced NLP Tasks**Xiaoya Li, Xiaofei Sun, Yuxian Meng, et al.*2020 · 734 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-dice-loss-data-imbalanced-nlp/)
28. [**Introduction to the Bio-entity Recognition Task at JNLPBA**Nigel Collier, Tomoko Ohta, Yoshimasa Tsuruoka, et al.*2004 · 731 citations*](https://md.research.elamur.ai/free/en/papers/collier-2004-introduction-bio-entity-recognition-task/)
29. [**The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies**Mihai Surdeanu, Richard Johansson, Adam Meyers, et al.*2008 · 728 citations*](https://md.research.elamur.ai/free/en/papers/surdeanu-2008-conll-2008-shared-task-joint/)
30. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
31. [**FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 645 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/)
32. [**A Linear Programming Formulation for Global Inference in Natural Language Tasks**Dan Roth, Wen-tau Yih*2004 · 636 citations*](https://md.research.elamur.ai/free/en/papers/roth-2004-linear-programming-formulation-global-inference/)
33. [**A Frustratingly Easy Approach for Entity and Relation Extraction**Zexuan Zhong, Danqi Chen*2020 · 629 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)
34. [**Harnessing Deep Neural Networks with Logic Rules**Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al.*2016 · 619 citations*](https://md.research.elamur.ai/free/en/papers/hu-2016-harnessing-deep-neural-networks-logic/)
35. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
36. [**Named Entity Recognition as Dependency Parsing**Juntao Yu, Bernd Bohnet, Massimo Poesio*2020 · 594 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-named-entity-recognition-as-dependency/)
37. [**Dissecting Contextual Word Embeddings: Architecture and Representation**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 585 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/)
38. [**A Survey on Recent Advances in Named Entity Recognition from Deep Learning models**Vikas Yadav, Steven Bethard*2018 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yadav-2018-survey-recent-advances-named-entity/)
39. [**Fast and Accurate Entity Recognition with Iterated Dilated Convolutions**Emma Strubell, Patrick Verga, David Belanger, et al.*2017 · 544 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2017-fast-accurate-entity-recognition-iterated/)
40. [**Semi-Supervised Sequence Modeling with Cross-View Training**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 489 citations*](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/)
41. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT**Shijie Wu, Mark Dredze*2019 · 466 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
42. [**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2021 · 464 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-p-tuning-v2-prompt-tuning/)
43. [**GPT-NER: Named Entity Recognition via Large Language Models**Shuhe Wang, Xiaofei Sun, Xiaoya Li, et al.*2025 · 436 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-gpt-ner-named-entity-recognition/)
44. [**Recognizing Named Entities in Tweets**Xiaohua Liu, Shaodian Zhang, Furu Wei, et al.*2011 · 435 citations*](https://md.research.elamur.ai/free/en/papers/liu-2011-recognizing-named-entities-tweets/)
45. [**Template-Based Named Entity Recognition Using BART**Leyang Cui, Yu Wu, Jian Liu, et al.*2021 · 420 citations*](https://md.research.elamur.ai/free/en/papers/cui-2021-template-based-named-entity-recognition/)
46. [**Empower Sequence Labeling with Task-Aware Neural Language Model**Liyuan Liu, Jingbo Shang, Frank F. Xu, et al.*2017 · 419 citations*](https://md.research.elamur.ai/free/en/papers/liu-2017-empower-sequence-labeling-task-aware/)
47. [**Nested Named Entity Recognition**Jenny Rose Finkel, Christopher D. Manning*2009 · 403 citations*](https://md.research.elamur.ai/free/en/papers/finkel-2009-nested-named-entity-recognition/)
48. [**Neural Architectures for Nested NER through Linearization**Jana Straková, Milan Straka, Jan Hajic*2019 · 400 citations*](https://md.research.elamur.ai/free/en/papers/strakova-2019-neural-architectures-nested-ner-through/)
49. [**Entity Disambiguation for Knowledge Base Population**Mark Dredze, Paul McNamee, Delip Rao, et al.*2010 · 394 citations*](https://md.research.elamur.ai/free/en/papers/dredze-2010-entity-disambiguation-knowledge-base-population/)
50. [**A Unified Generative Framework for Various NER Subtasks**Hang Yan, Tao Gui, Junqi Dai, et al.*2021 · 392 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-various-ner/)
51. [**Pooled Contextualized Embeddings for Named Entity Recognition**Alan Akbik, Tanja Bergmann, Roland Vollgraf*2019 · 385 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-pooled-contextualized-embeddings-named-entity/)
52. [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models**Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li*2023 · 379 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
53. [**Fine-Grained Analysis of Propaganda in News Articles**Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño, et al.*2019 · 379 citations*](https://md.research.elamur.ai/free/en/papers/martino-2019-fine-grained-analysis-propaganda-news/)
54. [**Emerging Cross-lingual Structure in Pretrained Language Models**Alexis Conneau, Shijie Wu, Haoran Li, et al.*2020 · 378 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/)
55. [**An Analysis of Encoder Representations in Transformer-Based Machine Translation**Alessandro Raganato, Jörg Tiedemann*2018 · 366 citations*](https://md.research.elamur.ai/free/en/papers/raganato-2018-analysis-encoder-representations-transformer-based/)
56. [**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/)
57. [**Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure**Oscar Täckström, Ryan McDonald, Jakob Uszkoreit*2012 · 340 citations*](https://md.research.elamur.ai/free/en/papers/tackstrom-2012-cross-lingual-word-clusters-direct/)
58. [**Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings**Thomas Manzini, Lim Yao Chong, Alan W Black, et al.*2019 · 332 citations*](https://md.research.elamur.ai/free/en/papers/manzini-2019-black-is-criminal-as-caucasian/)
59. [**Massively Multilingual Transfer for NER**Afshin Rahimi, Yuan Li, Trevor Cohn*2019 · 328 citations*](https://md.research.elamur.ai/free/en/papers/rahimi-2019-massively-multilingual-transfer-ner/)
60. [**ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation**Yu Sun, Shuohuan Wang, Shikun Feng, et al.*2021 · 324 citations*](https://md.research.elamur.ai/free/en/papers/sun-2021-ernie-3-0-large/)
61. [**LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention**Renrui Zhang, Jiaming Han, Chris Liu, et al.*2023 · 322 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llama-adapter-efficient-fine-tuning/)
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63. [**ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT**Xiang Wei; Xingyu Cui; Ning Cheng; Xiaobin Wang; Xin Zhang; Shen Huang; Pengjun Xie; Jinan Xu; Yufeng Chen; Meishan Zhang; Yong Jiang;, Wenjuan Han*2023 · 272 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-chatie-zero-shot-information-extraction/)
64. [**Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network**Yutai Hou, Wanxiang Che, Yongkui Lai, et al.*2020 · 265 citations*](https://md.research.elamur.ai/free/en/papers/hou-2020-few-shot-slot-tagging-collapsed/)
65. [**DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models**Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
66. [**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/)
67. [**Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling**Xiaochuang Han, Jacob Eisenstein*2019 · 242 citations*](https://md.research.elamur.ai/free/en/papers/han-2019-unsupervised-domain-adaptation-contextualized-embeddings/)
68. [**CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning**Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca Passonneau, et al.*2022 · 233 citations*](https://md.research.elamur.ai/free/en/papers/das-2022-container-few-shot-named-entity/)
69. [**Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition**Jana Straková, Milan Straka, Jan Hajič*2014 · 217 citations*](https://md.research.elamur.ai/free/en/papers/strakova-2014-open-source-tools-morphology-lemmatization/)
70. [**Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/)
71. [**LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders**Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.*2024 · 209 citations*](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/)
72. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
73. [**Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning**Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, et al.*2022 · 195 citations*](https://md.research.elamur.ai/free/en/papers/alabi-2022-adapting-pre-trained-language-models/)
74. [**InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction**Xiao Wang, Weikang Zhou, Can Zu, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/)
75. [**Time-Aware Language Models as Temporal Knowledge Bases**Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, et al.*2022 · 194 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2022-time-aware-language-models-as/)
76. [**Exploring and Predicting Transferability across NLP Tasks**Tu Vu, Tong Wang, Tsendsuren Munkhdalai, et al.*2020 · 192 citations*](https://md.research.elamur.ai/free/en/papers/vu-2020-exploring-predicting-transferability-across-nlp/)
77. [**Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation**Adam Poliak, Aparajita Haldar, Rachel Rudinger, et al.*2018 · 190 citations*](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/)
78. [**GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer**Urchade Zaratiana, Nadi Tomeh, Pierre Holat, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/zaratiana-2024-gliner-generalist-model-named-entity/)
79. [**DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension**Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, et al.*2018 · 188 citations*](https://md.research.elamur.ai/free/en/papers/saha-2018-duorc-complex-language-understanding-paraphrased/)
80. [**MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition**Shervin Malmasi, Anjie Fang, Besnik Fetahu, et al.*2022 · 181 citations*](https://md.research.elamur.ai/free/en/papers/malmasi-2022-multiconer-large-scale-multilingual-dataset/)
81. [**Planning with Learned Entity Prompts for Abstractive Summarization**Shashi Narayan, Yao Zhao, Joshua Maynez, et al.*2021 · 180 citations*](https://md.research.elamur.ai/free/en/papers/narayan-2021-planning-learned-entity-prompts-abstractive/)
82. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
83. [**Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP**Rob van der Goot, Ahmet Üstün, Alan Ramponi, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/goot-2021-massive-choice-ample-tasks-machamp/)
84. [**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/)
85. [**Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing**Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2021-trankit-light-weight-transformer-based/)
86. [**Tuning Multilingual Transformers for Language-Specific Named Entity Recognition**Mikhail Arkhipov, Maria Trofimova, Yuri Kuratov, et al.*2019 · 163 citations*](https://md.research.elamur.ai/free/en/papers/arkhipov-2019-tuning-multilingual-transformers-language-specific/)
87. [**Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 156 citations*](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/)
88. [**MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition**David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2022-masakhaner-2-0-africa-centric/)
89. [**How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/)
90. [**Context-faithful Prompting for Large Language Models**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/)
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