---
title: "Massachusetts Institute of Technology — works"
description: "125 works by researchers of Massachusetts Institute of Technology in our library · page 1 of 2, the most cited first."
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# Works of Massachusetts Institute of Technology

125 works · page 1 of 2 · the most cited first · [← Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/)

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1. [**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/)
2. [**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/)
3. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,030 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
4. [**Statistical Significance Tests for Machine Translation Evaluation**Philipp Koehn*2004 · 2,444 citations*](https://md.research.elamur.ai/free/en/papers/koehn-2004-statistical-significance-tests-machine-translation/)
5. [**Head-Driven Statistical Models for Natural Language Parsing**Michael Collins*2003 · 2,001 citations*](https://md.research.elamur.ai/free/en/papers/collins-2003-head-driven-statistical-models-natural/)
6. [**Making Pre-trained Language Models Better Few-shot Learners**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,952 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/)
7. [**Towards Deep Learning Models Resistant to Adversarial Attacks**Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, et al.*2017 · 1,786 citations*](https://md.research.elamur.ai/free/en/papers/madry-2017-towards-deep-learning-models-resistant/)
8. [**GLM: General Language Model Pretraining with Autoregressive Blank Infilling**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,449 citations*](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/)
9. [**Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment**Di Jin, Zhijing Jin, Joey Tianyi Zhou, et al.*2019 · 1,318 citations*](https://md.research.elamur.ai/free/en/papers/jin-2019-bert-really-robust-strong-baseline/)
10. [**Publicly Available Clinical BERT Embeddings**Emily Alsentzer, John R. Murphy, Willie Boag, et al.*2019 · 1,209 citations*](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
11. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,131 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/)
12. [**GPT Understands, Too**Xiao Liu, Yanan Zheng, Zhengxiao Du, et al.*2021 · 1,039 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-gpt-understands-too/)
13. [**A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents**Arman Cohan, Franck Dernoncourt, Doo Soon Kim, et al.*2018 · 1,018 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2018-discourse-aware-attention-model-abstractive/)
14. [**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/)
15. [**Discriminative Reranking for Natural Language Parsing**Michael Collins, Terry Koo*2000 · 988 citations*](https://md.research.elamur.ai/free/en/papers/collins-2000-discriminative-reranking-natural-language-parsing/)
16. [**Rationalizing Neural Predictions**Tao Lei, Regina Barzilay, Tommi Jaakkola*2016 · 928 citations*](https://md.research.elamur.ai/free/en/papers/lei-2016-rationalizing-neural-predictions/)
17. [**Clause Restructuring for Statistical Machine Translation**Michael Collins, Philipp Koehn, Ivona Kučerová*2005 · 918 citations*](https://md.research.elamur.ai/free/en/papers/collins-2005-clause-restructuring-statistical-machine-translation/)
18. [**Modeling Local Coherence: An Entity-Based Approach**Regina Barzilay, Mirella Lapata*2008 · 912 citations*](https://md.research.elamur.ai/free/en/papers/barzilay-2008-modeling-local-coherence-entity-based/)
19. [**TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP**John X. Morris, Eli Lifland, Jin Yong Yoo, et al.*2020 · 857 citations*](https://md.research.elamur.ai/free/en/papers/morris-2020-textattack-framework-adversarial-attacks-data/)
20. [**Analysis Methods in Neural Language Processing: A Survey**Yonatan Belinkov, James Glass*2019 · 735 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/)
21. [**Simple Semi-supervised Dependency Parsing**Terry Koo, Xavier Carreras, Michael Collins*2008 · 655 citations*](https://md.research.elamur.ai/free/en/papers/koo-2008-simple-semi-supervised-dependency-parsing/)
22. [**Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm**Bjarke Felbo, Alan Mislove, Anders Søgaard, et al.*2017 · 617 citations*](https://md.research.elamur.ai/free/en/papers/felbo-2017-millions-emoji-occurrences-learn-any/)
23. [**How Powerful are Graph Neural Networks?**Keyulu Xu, Weihua Hu, Jure Leskovec, et al.*2018 · 591 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-powerful-graph-neural-networks/)
24. [**ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 571 citations*](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/)
25. [**Incremental Parsing with the Perceptron Algorithm**Michael Collins, Brian Roark*2004 · 568 citations*](https://md.research.elamur.ai/free/en/papers/collins-2004-incremental-parsing-perceptron-algorithm/)
26. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 564 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
27. [**Sentence Fusion for Multidocument News Summarization**Regina Barzilay, Kathleen R. McKeown*2005 · 548 citations*](https://md.research.elamur.ai/free/en/papers/barzilay-2005-sentence-fusion-multidocument-news-summarization/)
28. [**Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation**Philipp Koehn, Amittai Axelrod, Alexandra Birch Mayne, et al.*2005 · 545 citations*](https://md.research.elamur.ai/free/en/papers/koehn-2005-edinburgh-system-description-2005-iwslt/)
29. [**LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code**Naman Jain, King Han, Alex Gu, et al.*2024 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/)
30. [**What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams**Di Jin, Eileen Pan, Nassim Oufattole, et al.*2020 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/)
31. [**Learning to Solve Arithmetic Word Problems with Verb Categorization**Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, et al.*2014 · 513 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/)
32. [**Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks**Ji Young Lee, Franck Dernoncourt*2016 · 509 citations*](https://md.research.elamur.ai/free/en/papers/lee-2016-sequential-short-text-classification-recurrent/)
33. [**Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses**Ryan Lowe, Michael Noseworthy, Iulian V. Serban, et al.*2017 · 486 citations*](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/)
34. [**What do Neural Machine Translation Models Learn about Morphology?**Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2017-what-do-neural-machine-translation/)
35. [**Large language models are few-shot clinical information extractors**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 448 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/)
36. [**Show your work: Scratchpads for intermediate computation with language models***2021 · 448 citations*](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/)
37. [**Analyzing the Structure of Attention in a Transformer Language Model**Jesse Vig, Yonatan Belinkov*2019 · 430 citations*](https://md.research.elamur.ai/free/en/papers/vig-2019-analyzing-structure-attention-transformer-language/)
38. [**Frame-Semantic Parsing**Dipanjan Das, Desai Chen, André F. T. Martins, et al.*2014 · 411 citations*](https://md.research.elamur.ai/free/en/papers/das-2014-frame-semantic-parsing/)
39. [**Aligning AI With Shared Human Values**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 382 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/)
40. [**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/)
41. [**Representing Discourse Coherence: A Corpus-Based Study**Florian Wolf, Edward Gibson*2005 · 377 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2005-representing-discourse-coherence-corpus-based/)
42. [**MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension**Adam Fisch, Alon Talmor, Robin Jia, et al.*2019 · 375 citations*](https://md.research.elamur.ai/free/en/papers/fisch-2019-mrqa-2019-shared-task-evaluating/)
43. [**Experiments with a Higher-Order Projective Dependency Parser**Xavier Carreras*2007 · 358 citations*](https://md.research.elamur.ai/free/en/papers/carreras-2007-experiments-higher-order-projective-dependency/)
44. [**Parameter-Efficient Transfer Learning with Diff Pruning**Demi Guo, Alexander Rush, Yoon Kim*2021 · 355 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/)
45. [**Predicting Factuality of Reporting and Bias of News Media Sources**Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, et al.*2018 · 334 citations*](https://md.research.elamur.ai/free/en/papers/baly-2018-predicting-factuality-reporting-bias-news/)
46. [**RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 327 citations*](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/)
47. [**Paraphrasing for Automatic Evaluation**David Kauchak, Regina Barzilay*2006 · 321 citations*](https://md.research.elamur.ai/free/en/papers/kauchak-2006-paraphrasing-automatic-evaluation/)
48. [**SemEval-2016 Task 3: Community Question Answering**Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, et al.*2016 · 316 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-3-community/)
49. [**Chinese Syntactic Reordering for Statistical Machine Translation**Chao Wang, Michael Collins, Philipp Koehn*2007 · 314 citations*](https://md.research.elamur.ai/free/en/papers/wang-2007-chinese-syntactic-reordering-statistical-machine/)
50. [**Good-Enough Compositional Data Augmentation**Jacob Andreas*2020 · 305 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2020-good-enough-compositional-data-augmentation/)
51. [**Towards Debiasing Fact Verification Models**Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, et al.*2019 · 296 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2019-debiasing-fact-verification-models/)
52. [**Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing**Tal Schuster, Ori Ram, Regina Barzilay, et al.*2019 · 294 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-alignment-contextual-word/)
53. [**Neural language models as psycholinguistic subjects: Representations of syntactic state**Richard Futrell, Ethan Wilcox, Takashi Morita, et al.*2019 · 291 citations*](https://md.research.elamur.ai/free/en/papers/futrell-2019-neural-language-models-as-psycholinguistic/)
54. [**Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 289 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/)
55. [**From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference**Siddharth Samsi, Dan Zhao, Joseph McDonald, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/samsi-2023-words-watts-benchmarking-energy-costs/)
56. [**emrQA: A Large Corpus for Question Answering on Electronic Medical Records**Anusri Pampari, Preethi Raghavan, Jennifer Liang, et al.*2018 · 285 citations*](https://md.research.elamur.ai/free/en/papers/pampari-2018-emrqa-large-corpus-question-answering/)
57. [**What do RNN Language Models Learn about Filler-Gap Dependencies?**Ethan Wilcox, Roger Levy, Takashi Morita, et al.*2018 · 276 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2018-rnn-language-models-learn-about/)
58. [**DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings**Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, et al.*2022 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2022-diffcse-difference-based-contrastive-learning/)
59. [**LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models**Yukang Chen, Shengju Qian, Haotian Tang, et al.*2023 · 265 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-longlora-efficient-fine-tuning-long/)
60. [**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/)
61. [**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/)
62. [**Selective Sharing for Multilingual Dependency Parsing**Tahira Naseem, Regina Barzilay, Amir Globerson*2012 · 237 citations*](https://md.research.elamur.ai/free/en/papers/naseem-2012-selective-sharing-multilingual-dependency-parsing/)
63. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
64. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
65. [**What learning algorithm is in-context learning? Investigations with linear models**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 224 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
66. [**Unsupervised Multilingual Learning for Morphological Segmentation**Benjamin Snyder, Regina Barzilay*2008 · 221 citations*](https://md.research.elamur.ai/free/en/papers/snyder-2008-unsupervised-multilingual-learning-morphological-segmentation/)
67. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 220 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
68. [**Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing**Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, et al.*2019 · 217 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2019-modeling-language-variation-universals-survey/)
69. [**Nonliteral understanding of number words**Justine T. Kao, Jean Y. Wu, Leon Bergen, et al.*2014 · 213 citations*](https://md.research.elamur.ai/free/en/papers/kao-2014-nonliteral-understanding-number-words/)
70. [**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/)
71. [**Refusal in Language Models Is Mediated by a Single Direction**Andy Arditi, Oscar Obeso, Aaquib Syed, et al.*2024 · 188 citations*](https://md.research.elamur.ai/free/en/papers/arditi-2024-refusal-language-models-mediated-single/)
72. [**Integrating Stance Detection and Fact Checking in a Unified Corpus**Ramy Baly, Mitra Mohtarami, James Glass, et al.*2018 · 186 citations*](https://md.research.elamur.ai/free/en/papers/baly-2018-integrating-stance-detection-fact-checking/)
73. [**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/)
74. [**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
75. [**Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2018 · 169 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2018-language-identification-morphosyntactic-tagging-second/)
76. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
77. [**The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets**Samuel Marks, Max Tegmark*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/marks-2023-geometry-truth-emergent-linear-structure/)
78. [**Testing the Predictions of Surprisal Theory in 11 Languages**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/)
79. [**Automatic Stance Detection Using End-to-End Memory Networks**Mitra Mohtarami, Ramy Baly, James Glass, et al.*2018 · 156 citations*](https://md.research.elamur.ai/free/en/papers/mohtarami-2018-automatic-stance-detection-end-end/)
80. [**The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition**Ahmed Ali, Peter Bell, James Glass, et al.*2016 · 154 citations*](https://md.research.elamur.ai/free/en/papers/ali-2016-mgb-2-challenge-arabic-multi/)
81. [**Evaluating Style Transfer for Text**Remi Mir, Bjarke Felbo, Nick Obradovich, et al.*2019 · 154 citations*](https://md.research.elamur.ai/free/en/papers/mir-2019-evaluating-style-transfer-text/)
82. [**DART: Open-Domain Structured Data Record to Text Generation**Linyong Nan, Dragomir Radev, Rui Zhang, et al.*2020 · 154 citations*](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/)
83. [**SyntaxGym: An Online Platform for Targeted Evaluation of Language Models**Jon Gauthier, Jennifer Hu, Ethan Wilcox, et al.*2020 · 145 citations*](https://md.research.elamur.ai/free/en/papers/gauthier-2020-syntaxgym-online-platform-targeted-evaluation/)
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. [**Mapping to Declarative Knowledge for Word Problem Solving**Subhro Roy, Dan Roth*2018 · 143 citations*](https://md.research.elamur.ai/free/en/papers/roy-2018-mapping-declarative-knowledge-word-problem/)
86. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
87. [**A Roadmap to Pluralistic Alignment**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
88. [**Prompting is not a substitute for probability measurements in large language models**Jennifer Hu, Roger Levy*2023 · 133 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/)
89. [**Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 121 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/)
90. [**Sarcasm Analysis Using Conversation Context**Debanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan*2018 · 118 citations*](https://md.research.elamur.ai/free/en/papers/ghosh-2018-sarcasm-analysis-conversation-context/)
91. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/)
92. [**Universal Dependencies for Learner English**Yevgeni Berzak, Jessica Kenney, Carolyn Spadine, et al.*2016 · 109 citations*](https://md.research.elamur.ai/free/en/papers/berzak-2016-universal-dependencies-learner-english/)
93. [**Olmo 3**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
94. [**Data Engineering for Scaling Language Models to 128K Context**Yao Fu, Rameswar Panda, Xinyao Niu, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-data-engineering-scaling-language-models/)
95. [**End-to-End Bias Mitigation by Modelling Biases in Corpora**Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/)
96. [**Arabic Diacritization with Recurrent Neural Networks**Yonatan Belinkov, James Glass*2015 · 93 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2015-arabic-diacritization-recurrent-neural-networks/)
97. [**A Report on the 2020 Sarcasm Detection Shared Task**Debanjan Ghosh, Avijit Vajpayee, Smaranda Muresan*2020 · 92 citations*](https://md.research.elamur.ai/free/en/papers/ghosh-2020-report-2020-sarcasm-detection-shared/)
98. [**Machine Comprehension with Discourse Relations**Karthik Narasimhan, Regina Barzilay*2015 · 91 citations*](https://md.research.elamur.ai/free/en/papers/narasimhan-2015-machine-comprehension-discourse-relations/)
99. [**Pragmatically Informative Text Generation**Sheng Shen, Daniel Fried, Jacob Andreas, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/shen-2019-pragmatically-informative-text-generation/)
100. [**RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency**Richard Futrell, Ethan Wilcox, Takashi Morita, et al.*2018 · 75 citations*](https://md.research.elamur.ai/free/en/papers/futrell-2018-rnns-psycholinguistic-subjects-syntactic-state/)

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