---
title: "Computer Science — papers, page 2 — Elamur Research"
description: "Computer Science: 9,085 papers of the Elamur Research library · page 2 of 92 · 26–125, the most cited first — our summary of each, its…"
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# 🖥️ Computer Science

9,085 papers · page 2 of 92 · 26–125 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Artificial intelligence, language technology, vision, systems, networks, software and the theory of computing.

## Papers (9,085)

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26. [**Deep Contextualized Word Representations**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 14,852 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/)
27. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 13,703 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
28. [**Momentum Contrast for Unsupervised Visual Representation Learning**Kaiming He, Haoqi Fan, Yuxin Wu, et al.*2019 · 13,151 citations*](https://md.research.elamur.ai/free/en/papers/he-2019-momentum-contrast-unsupervised-visual-representation/)
29. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,784 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
30. [**GPT-4 Technical Report**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 12,732 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
31. [**The PageRank Citation Ranking: Bringing Order to the Web**Lawrence Page, Sergey Brin, Rajeev Motwani, et al.*1999 · 12,693 citations*](https://md.research.elamur.ai/free/en/papers/page-1999-pagerank-citation-ranking-bringing/)
32. [**FaceNet: A Unified Embedding for Face Recognition and Clustering**Florian Schroff, Dmitry Kalenichenko, James Philbin*2015 · 11,940 citations*](https://md.research.elamur.ai/free/en/papers/schroff-2015-facenet-unified-embedding-face-recognition/)
33. [**Enriching Word Vectors with Subword Information**Piotr Bojanowski, Edouard Grave, Armand Joulin, et al.*2017 · 11,848 citations*](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/)
34. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,308 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
35. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,088 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
36. [**Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead**Cynthia Rudin*2018 · 10,431 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/)
37. [**A Comprehensive Survey on Graph Neural Networks**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,300 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
38. [**Training language models to follow instructions with human feedback**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,005 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
39. [**MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications**Andrew G. Howard, Menglong Zhu, Bo Chen, et al.*2017 · 9,641 citations*](https://md.research.elamur.ai/free/en/papers/howard-2017-mobilenets-efficient-convolutional-neural-networks/)
40. [**SQuAD: 100,000+ Questions for Machine Comprehension of Text**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,513 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
41. [**LLaMA: Open and Efficient Foundation Language Models**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 9,472 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
42. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,464 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)
43. [**Authoritative Sources in a Hyperlinked Environment**Jon M. Kleinberg*1999 · 9,093 citations*](https://md.research.elamur.ai/free/en/papers/kleinberg-1999-authoritative-sources-hyperlinked-environment/)
44. [**Graph Attention Networks**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 9,000 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
45. [**Semi-Supervised Classification with Graph Convolutional Networks**Thomas N. Kipf, Max Welling*2016 · 8,826 citations*](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
46. [**The Stanford CoreNLP Natural Language Processing Toolkit**Christopher Manning, Mihai Surdeanu, John Bauer, et al.*2014 · 8,629 citations*](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
47. [**MIMIC-III, a freely accessible critical care database**Alistair E. W. Johnson, Tom Pollard, Lu Shen, et al.*2016 · 8,544 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/)
48. [**LoRA: Low-Rank Adaptation of Large Language Models**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 8,473 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
49. [**Explaining and Harnessing Adversarial Examples**Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy*2014 · 8,440 citations*](https://md.research.elamur.ai/free/en/papers/goodfellow-2014-explaining-harnessing-adversarial-examples/)
50. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,328 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
51. [**BioBERT: a pre-trained biomedical language representation model for biomedical text mining**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,311 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
52. [**Text Categorization with Support Vector Machines: Learning with Many Relevant Features**Thorsten Joachims*1998 · 8,311 citations*](https://md.research.elamur.ai/free/en/papers/joachims-1998-text-categorization-support-vector-machines/)
53. [**A Unified Approach to Interpreting Model Predictions**Scott Lundberg, Su-In Lee*2017 · 8,091 citations*](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/)
54. [**A Simple Framework for Contrastive Learning of Visual Representations**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 7,783 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
55. [**Thumbs up? Sentiment Classification using Machine Learning Techniques**Bo Pang, Lillian Lee, Shivakumar Vaithyanathan*2002 · 7,762 citations*](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
56. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,756 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
57. [**Show, Attend and Tell: Neural Image Caption Generation with Visual Attention**Kelvin Xu, Jimmy Ba, Ryan Kiros, et al.*2015 · 7,523 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
58. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,436 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
59. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
60. [**UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction**Leland McInnes, John Healy, James Melville*2018 · 7,138 citations*](https://md.research.elamur.ai/free/en/papers/mcinnes-2018-umap-uniform-manifold-approximation-projection/)
61. [**Representation Learning with Contrastive Predictive Coding**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,842 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
62. [**Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks**Alec Radford, Luke Metz, Soumith Chintala*2015 · 6,532 citations*](https://md.research.elamur.ai/free/en/papers/radford-2015-unsupervised-representation-learning-deep-convolutional/)
63. [**Learning Transferable Architectures for Scalable Image Recognition**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,521 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/)
64. [**Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization**Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, et al.*2016 · 6,330 citations*](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/)
65. [**Natural Language Processing (almost) from Scratch**Ronan Collobert, Jason Weston, Leon Bottou, et al.*2011 · 6,138 citations*](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)
66. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,957 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
67. [**Emerging Properties in Self-Supervised Vision Transformers**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 5,912 citations*](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/)
68. [**Intriguing properties of neural networks**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,878 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
69. [**Combining Labeled and Unlabeled Data with Co-Training**Avrim Blum, Tom Mitchell*1998 · 5,830 citations*](https://md.research.elamur.ai/free/en/papers/blum-1998-combining-labeled-unlabeled-data-co-training/)
70. [**Distributed Representations of Sentences and Documents**Quoc V. Le, Tomas Mikolov*2014 · 5,786 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
71. [**Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations**Ranjay Krishna, Yuke Zhu, Oliver Groth, et al.*2016 · 5,761 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/)
72. [**ADADELTA: An Adaptive Learning Rate Method**Matthew D. Zeiler*2012 · 5,658 citations*](https://md.research.elamur.ai/free/en/papers/zeiler-2012-adadelta-adaptive-learning-rate-method/)
73. [**A Survey on Bias and Fairness in Machine Learning**Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al.*2019 · 5,626 citations*](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/)
74. [**METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments**Satanjeev Banerjee, Alon Lavie*2005 · 5,504 citations*](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/)
75. [**Hierarchical Attention Networks for Document Classification**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,423 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
76. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,396 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
77. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
78. [**Explanation in Artificial Intelligence: Insights from the Social Sciences**Tim Miller*2017 · 5,364 citations*](https://md.research.elamur.ai/free/en/papers/miller-2017-explanation-artificial-intelligence-insights-social/)
79. [**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/)
80. [**Instance-Based Learning Algorithms**David W. Aha, Dennis Kibler, Marc K. Albert*1991 · 5,250 citations*](https://md.research.elamur.ai/free/en/papers/aha-1991-instance-based-learning-algorithms/)
81. [**WaveNet: A Generative Model for Raw Audio**Aaron van den Oord, Sander Dieleman, Heiga Zen, et al.*2016 · 5,190 citations*](https://md.research.elamur.ai/free/en/papers/oord-2016-wavenet-generative-model-raw-audio/)
82. [**Active Learning Literature Survey**Burr Settles*2009 · 4,890 citations*](https://md.research.elamur.ai/free/en/papers/settles-2009-active-learning-literature-survey/)
83. [**mixup: Beyond Empirical Risk Minimization**Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, et al.*2017 · 4,811 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-mixup-empirical-risk-minimization/)
84. [**Character-level Convolutional Networks for Text Classification**Xiang Zhang, Junbo Zhao, Yann LeCun*2015 · 4,781 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/)
85. [**Inductive Representation Learning on Large Graphs**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,696 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
86. [**Billion-scale similarity search with GPUs**Jeff Johnson, Matthijs Douze, Hervé Jégou*2017 · 4,691 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2017-billion-scale-similarity-search-gpus/)
87. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,680 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
88. [**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/)
89. [**Microsoft COCO: Common Objects in Context**Tsung-Yi Lin, Michael Maire, Serge Belongie, et al.*2014 · 4,567 citations*](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
90. [**Large Language Models Encode Clinical Knowledge**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,477 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/)
91. [**SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing**Taku Kudo, John Richardson*2018 · 4,471 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2018-sentencepiece-simple-language-independent-subword/)
92. [**Efficient Memory Management for Large Language Model Serving with PagedAttention**Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, et al.*2023 · 4,453 citations*](https://md.research.elamur.ai/free/en/papers/kwon-2023-efficient-memory-management-large-language/)
93. [**Word Association Norms, Mutual Information, and Lexicography**Kenneth Ward Church, Patrick Hanks*1990 · 4,368 citations*](https://md.research.elamur.ai/free/en/papers/church-1990-word-association-norms-mutual-information/)
94. [**Neural Architecture Search with Reinforcement Learning**Barret Zoph, Quoc V. Le*2016 · 4,339 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
95. [**Learning Word Vectors for Sentiment Analysis**Andrew L. Maas, Raymond E. Daly, Peter T. Pham, et al.*2011 · 4,290 citations*](https://md.research.elamur.ai/free/en/papers/maas-2011-learning-word-vectors-sentiment-analysis/)
96. [**Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models**Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al.*2023 · 4,168 citations*](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/)
97. [**A Call for Clarity in Reporting BLEU Scores**Matt Post*2018 · 4,135 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/)
98. [**Caffe: Convolutional Architecture for Fast Feature Embedding**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,071 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
99. [**Energy and Policy Considerations for Deep Learning in NLP**Emma Strubell, Ananya Ganesh, Andrew McCallum*2019 · 4,053 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/)
100. [**Natural Questions: A Benchmark for Question Answering Research**Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al.*2019 · 4,035 citations*](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
101. [**Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis**Theresa Wilson, Janyce Wiebe, Paul Hoffmann*2005 · 3,991 citations*](https://md.research.elamur.ai/free/en/papers/wilson-2005-recognizing-contextual-polarity-phrase-level/)
102. [**Neural Machine Translation of Rare Words with Subword Units**Rico Sennrich, Barry Haddow, Alexandra Birch*2016 · 3,980 citations*](https://md.research.elamur.ai/free/en/papers/sennrich-2016-neural-machine-translation-rare-words/)
103. [**fairseq: A Fast, Extensible Toolkit for Sequence Modeling**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,951 citations*](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/)
104. [**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/)
105. [**SimCSE: Simple Contrastive Learning of Sentence Embeddings**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,922 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/)
106. [**A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts**Bo Pang, Lillian Lee*2004 · 3,849 citations*](https://md.research.elamur.ai/free/en/papers/pang-2004-sentimental-education-sentiment-analysis-subjectivity/)
107. [**Longformer: The Long-Document Transformer**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 3,836 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/)
108. [**Teaching Machines to Read and Comprehend**Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, et al.*2015 · 3,829 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
109. [**A Study of Translation Edit Rate with Targeted Human Annotation**Matthew Snover, Bonnie Dorr, Rich Schwartz, et al.*2006 · 3,828 citations*](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/)
110. [**Bidirectional LSTM-CRF Models for Sequence Tagging**Zhiheng Huang, Wei Xu, Kai Yu*2015 · 3,818 citations*](https://md.research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/)
111. [**SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition**Daniel S. Park, William Chan, Yu Zhang, et al.*2019 · 3,809 citations*](https://md.research.elamur.ai/free/en/papers/park-2019-specaugment-simple-data-augmentation-method/)
112. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,795 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
113. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,789 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
114. [**Neural Message Passing for Quantum Chemistry**Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, et al.*2017 · 3,703 citations*](https://md.research.elamur.ai/free/en/papers/gilmer-2017-neural-message-passing-quantum-chemistry/)
115. [**Training Verifiers to Solve Math Word Problems**Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.*2021 · 3,680 citations*](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/)
116. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,641 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
117. [**A Maximum Entropy Approach to Natural Language Processing**Adam L. Berger, Stephen A. Della Pietra, Vincent J. Della Pietra*1996 · 3,638 citations*](https://md.research.elamur.ai/free/en/papers/berger-1996-maximum-entropy-approach-natural-language/)
118. [**Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling**Jenny Rose Finkel, Trond Grenager, Christopher Manning*2005 · 3,622 citations*](https://md.research.elamur.ai/free/en/papers/finkel-2005-incorporating-non-local-information-into/)
119. [**Categorical Reparameterization with Gumbel-Softmax**Eric Jang, Shixiang Gu, Ben Poole*2016 · 3,577 citations*](https://md.research.elamur.ai/free/en/papers/jang-2016-categorical-reparameterization-gumbel-softmax/)
120. [**DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia**Jens Lehmann, Robert Isele, Max Jakob, et al.*2015 · 3,571 citations*](https://md.research.elamur.ai/free/en/papers/lehmann-2015-dbpedia-large-scale-multilingual-knowledge/)
121. [**SciBERT: A Pretrained Language Model for Scientific Text**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,567 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
122. [**Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network**Kristina Toutanova, Dan Klein, Christopher D. Manning, et al.*2003 · 3,566 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/)
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