---
title: "Artificial Intelligence — papers, page 2"
description: "Artificial Intelligence: 7,180 papers of the Elamur Research library · page 2 of 73 · 26–125, the most cited first — our summary of each, its ideas…"
canonical: "https://research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/page/2/"
---
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# 🤖 Artificial Intelligence

7,180 papers · page 2 of 73 · 26–125 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Artificial Intelligence is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Natural Language Processing Techniques», «Topic Modeling», «Speech and dialogue systems».

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) [1](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) **2** [3](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/page/3/) … [73](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/page/73/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/page/3/)

26. [**Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead**Cynthia Rudin*2018 · 10,427 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/)
27. [**A Comprehensive Survey on Graph Neural Networks**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,295 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
28. [**MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications**Andrew G. Howard, Menglong Zhu, Bo Chen, et al.*2017 · 9,633 citations*](https://md.research.elamur.ai/free/en/papers/howard-2017-mobilenets-efficient-convolutional-neural-networks/)
29. [**SQuAD: 100,000+ Questions for Machine Comprehension of Text**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,488 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
30. [**Graph Attention Networks**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 8,996 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
31. [**Semi-Supervised Classification with Graph Convolutional Networks**Thomas N. Kipf, Max Welling*2016 · 8,809 citations*](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
32. [**The Stanford CoreNLP Natural Language Processing Toolkit**Christopher Manning, Mihai Surdeanu, John Bauer, et al.*2014 · 8,620 citations*](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
33. [**MIMIC-III, a freely accessible critical care database**Alistair E. W. Johnson, Tom Pollard, Lu Shen, et al.*2016 · 8,530 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/)
34. [**Explaining and Harnessing Adversarial Examples**Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy*2014 · 8,430 citations*](https://md.research.elamur.ai/free/en/papers/goodfellow-2014-explaining-harnessing-adversarial-examples/)
35. [**Text Categorization with Support Vector Machines: Learning with Many Relevant Features**Thorsten Joachims*1998 · 8,304 citations*](https://md.research.elamur.ai/free/en/papers/joachims-1998-text-categorization-support-vector-machines/)
36. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
37. [**BioBERT: a pre-trained biomedical language representation model for biomedical text mining**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,281 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
38. [**A Unified Approach to Interpreting Model Predictions**Scott Lundberg, Su-In Lee*2017 · 8,086 citations*](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/)
39. [**A Simple Framework for Contrastive Learning of Visual Representations**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 7,774 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
40. [**Thumbs up? Sentiment Classification using Machine Learning Techniques**Bo Pang, Lillian Lee, Shivakumar Vaithyanathan*2002 · 7,756 citations*](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
41. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,734 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
42. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 7,433 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
43. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,429 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
44. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
45. [**UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction**Leland McInnes, John Healy, James Melville*2018 · 7,136 citations*](https://md.research.elamur.ai/free/en/papers/mcinnes-2018-umap-uniform-manifold-approximation-projection/)
46. [**Representation Learning with Contrastive Predictive Coding**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,826 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
47. [**Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks**Alec Radford, Luke Metz, Soumith Chintala*2015 · 6,519 citations*](https://md.research.elamur.ai/free/en/papers/radford-2015-unsupervised-representation-learning-deep-convolutional/)
48. [**Learning Transferable Architectures for Scalable Image Recognition**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,518 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/)
49. [**Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization**Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, et al.*2016 · 6,327 citations*](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/)
50. [**Natural Language Processing (almost) from Scratch**Ronan Collobert, Jason Weston, Leon Bottou, et al.*2011 · 6,099 citations*](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)
51. [**LoRA: Low-Rank Adaptation of Large Language Models**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 5,972 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
52. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
53. [**Emerging Properties in Self-Supervised Vision Transformers**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 5,910 citations*](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/)
54. [**Intriguing properties of neural networks**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,870 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
55. [**Combining Labeled and Unlabeled Data with Co-Training**Avrim Blum, Tom Mitchell*1998 · 5,829 citations*](https://md.research.elamur.ai/free/en/papers/blum-1998-combining-labeled-unlabeled-data-co-training/)
56. [**Training language models to follow instructions with human feedback**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 5,770 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
57. [**Distributed Representations of Sentences and Documents**Quoc V. Le, Tomas Mikolov*2014 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
58. [**ADADELTA: An Adaptive Learning Rate Method**Matthew D. Zeiler*2012 · 5,650 citations*](https://md.research.elamur.ai/free/en/papers/zeiler-2012-adadelta-adaptive-learning-rate-method/)
59. [**A Survey on Bias and Fairness in Machine Learning**Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al.*2019 · 5,622 citations*](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/)
60. [**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/)
61. [**METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments**Satanjeev Banerjee, Alon Lavie*2005 · 5,483 citations*](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/)
62. [**Hierarchical Attention Networks for Document Classification**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,409 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
63. [**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/)
64. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,370 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
65. [**Explanation in Artificial Intelligence: Insights from the Social Sciences**Tim Miller*2017 · 5,362 citations*](https://md.research.elamur.ai/free/en/papers/miller-2017-explanation-artificial-intelligence-insights-social/)
66. [**EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**Mingxing Tan, Quoc V. Le*2019 · 5,308 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/)
67. [**Instance-Based Learning Algorithms**David W. Aha, Dennis Kibler, Marc K. Albert*1991 · 5,248 citations*](https://md.research.elamur.ai/free/en/papers/aha-1991-instance-based-learning-algorithms/)
68. [**WaveNet: A Generative Model for Raw Audio**Aaron van den Oord, Sander Dieleman, Heiga Zen, et al.*2016 · 5,167 citations*](https://md.research.elamur.ai/free/en/papers/oord-2016-wavenet-generative-model-raw-audio/)
69. [**Active Learning Literature Survey**Burr Settles*2009 · 4,889 citations*](https://md.research.elamur.ai/free/en/papers/settles-2009-active-learning-literature-survey/)
70. [**mixup: Beyond Empirical Risk Minimization**Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, et al.*2017 · 4,803 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-mixup-empirical-risk-minimization/)
71. [**Character-level Convolutional Networks for Text Classification**Xiang Zhang, Junbo Zhao, Yann LeCun*2015 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/)
72. [**Inductive Representation Learning on Large Graphs**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
73. [**DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,593 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)
74. [**Large Language Models Encode Clinical Knowledge**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,464 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/)
75. [**SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing**Taku Kudo, John Richardson*2018 · 4,461 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2018-sentencepiece-simple-language-independent-subword/)
76. [**Word Association Norms, Mutual Information, and Lexicography**Kenneth Ward Church, Patrick Hanks*1990 · 4,363 citations*](https://md.research.elamur.ai/free/en/papers/church-1990-word-association-norms-mutual-information/)
77. [**Neural Architecture Search with Reinforcement Learning**Barret Zoph, Quoc V. Le*2016 · 4,331 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
78. [**Learning Word Vectors for Sentiment Analysis**Andrew L. Maas, Raymond E. Daly, Peter T. Pham, et al.*2011 · 4,272 citations*](https://md.research.elamur.ai/free/en/papers/maas-2011-learning-word-vectors-sentiment-analysis/)
79. [**Distributed Representations of Words and Phrases and their Compositionality**Tomas Mikolov, Ilya Sutskever, Kai Chen, et al.*2013 · 4,233 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/)
80. [**A Call for Clarity in Reporting BLEU Scores**Matt Post*2018 · 4,130 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/)
81. [**Caffe: Convolutional Architecture for Fast Feature Embedding**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,064 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
82. [**Energy and Policy Considerations for Deep Learning in NLP**Emma Strubell, Ananya Ganesh, Andrew McCallum*2019 · 4,049 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/)
83. [**Natural Questions: A Benchmark for Question Answering Research**Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al.*2019 · 4,031 citations*](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
84. [**Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis**Theresa Wilson, Janyce Wiebe, Paul Hoffmann*2005 · 3,979 citations*](https://md.research.elamur.ai/free/en/papers/wilson-2005-recognizing-contextual-polarity-phrase-level/)
85. [**Accurate Unlexicalized Parsing**Dan Klein, Christopher D. Manning*2003 · 3,942 citations*](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/)
86. [**fairseq: A Fast, Extensible Toolkit for Sequence Modeling**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,941 citations*](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/)
87. [**SimCSE: Simple Contrastive Learning of Sentence Embeddings**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,921 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/)
88. [**Efficient Estimation of Word Representations in Vector Space**Tomas Mikolov, Kai Chen, Greg Corrado, et al.*2013 · 3,846 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/)
89. [**A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts**Bo Pang, Lillian Lee*2004 · 3,842 citations*](https://md.research.elamur.ai/free/en/papers/pang-2004-sentimental-education-sentiment-analysis-subjectivity/)
90. [**A Study of Translation Edit Rate with Targeted Human Annotation**Matthew Snover, Bonnie Dorr, Rich Schwartz, et al.*2006 · 3,824 citations*](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/)
91. [**Teaching Machines to Read and Comprehend**Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, et al.*2015 · 3,806 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
92. [**SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition**Daniel S. Park, William Chan, Yu Zhang, et al.*2019 · 3,802 citations*](https://md.research.elamur.ai/free/en/papers/park-2019-specaugment-simple-data-augmentation-method/)
93. [**Bidirectional LSTM-CRF Models for Sequence Tagging**Zhiheng Huang, Wei Xu, Kai Yu*2015 · 3,802 citations*](https://md.research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/)
94. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,780 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
95. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,740 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
96. [**Training Verifiers to Solve Math Word Problems**Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.*2021 · 3,675 citations*](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/)
97. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
98. [**A Maximum Entropy Approach to Natural Language Processing**Adam L. Berger, Stephen A. Della Pietra, Vincent J. Della Pietra*1996 · 3,633 citations*](https://md.research.elamur.ai/free/en/papers/berger-1996-maximum-entropy-approach-natural-language/)
99. [**Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling**Jenny Rose Finkel, Trond Grenager, Christopher Manning*2005 · 3,618 citations*](https://md.research.elamur.ai/free/en/papers/finkel-2005-incorporating-non-local-information-into/)
100. [**Categorical Reparameterization with Gumbel-Softmax**Eric Jang, Shixiang Gu, Ben Poole*2016 · 3,571 citations*](https://md.research.elamur.ai/free/en/papers/jang-2016-categorical-reparameterization-gumbel-softmax/)
101. [**Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network**Kristina Toutanova, Dan Klein, Christopher D. Manning, et al.*2003 · 3,559 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/)
102. [**Lexicon-Based Methods for Sentiment Analysis**Maite Taboada, Julian Brooke, Milan Tofiloski, et al.*2011 · 3,556 citations*](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/)
103. [**SciBERT: A Pretrained Language Model for Scientific Text**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,548 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
104. [**The Berkeley FrameNet Project**Collin F. Baker, Charles J. Fillmore, John B. Lowe*1998 · 3,544 citations*](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/)
105. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
106. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
107. [**Linguistic Regularities in Continuous Space Word Representations**Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig*2013 · 3,482 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-linguistic-regularities-continuous-space-word/)
108. [**HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units**Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.*2021 · 3,441 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2021-hubert-self-supervised-speech-representation/)
109. [**Conformer: Convolution-augmented Transformer for Speech Recognition**Anmol Gulati, James Qin, Chung-Cheng Chiu, et al.*2020 · 3,433 citations*](https://md.research.elamur.ai/free/en/papers/gulati-2020-conformer-convolution-augmented-transformer-speech/)
110. [**Using Information Content to Evaluate Semantic Similarity in a Taxonomy**Philip Resnik*1995 · 3,419 citations*](https://md.research.elamur.ai/free/en/papers/resnik-1995-information-content-evaluate-semantic-similarity/)
111. [**Neural Machine Translation of Rare Words with Subword Units**Rico Sennrich, Barry Haddow, Alexandra Birch*2016 · 3,409 citations*](https://md.research.elamur.ai/free/en/papers/sennrich-2016-neural-machine-translation-rare-words/)
112. [**MIMIC-IV, a freely accessible electronic health record dataset**Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, et al.*2023 · 3,385 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/)
113. [**Know What You Don't Know: Unanswerable Questions for SQuAD**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,360 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/)
114. [**Qwen3 Technical Report**An Yang, Anfeng Li, Baosong Yang, et al.*2025 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
115. [**Verb Semantics and Lexical Selection**Zhibiao Wu, Martha Palmer*1994 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/wu-1994-verb-semantics-lexical-selection/)
116. [**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/)
117. [**A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,221 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
118. [**Continual Lifelong Learning with Neural Networks: A Review**German I. Parisi, Ronald Kemker, Jose L. Part, et al.*2018 · 3,210 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/)
119. [**The Proposition Bank: An Annotated Corpus of Semantic Roles**Martha Palmer, Daniel Gildea, Paul Kingsbury*2005 · 3,208 citations*](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/)
120. [**TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,204 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
121. [**Towards A Rigorous Science of Interpretable Machine Learning**Finale Doshi-Velez, Been Kim*2017 · 3,202 citations*](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/)
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