---
title: "Harvard University — works"
description: "64 works by researchers of Harvard University in our library, the most cited first."
canonical: "https://research.elamur.ai/free/en/universities/harvard-university/works/"
---
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# Works of Harvard University

64 works · the most cited first · [← Harvard University](https://md.research.elamur.ai/free/en/universities/harvard-university/)

1. [**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 · 4,154 citations*](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/)
2. [**OpenNMT: Open-Source Toolkit for Neural Machine Translation**Guillaume Klein, Yoon Kim, Yuntian Deng, et al.*2017 · 2,460 citations*](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/)
3. [**Centering: A Framework for Modeling the Local Coherence of Discourse**Barbara J. Grosz, Aravind K. Joshi, Scott Weinstein*1995 · 2,101 citations*](https://md.research.elamur.ai/free/en/papers/grosz-1995-centering-framework-modeling-local-coherence/)
4. [**Character-Aware Neural Language Models**Yoon Kim, Yacine Jernite, David Sontag, et al.*2015 · 1,852 citations*](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/)
5. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
6. [**ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks**Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli*2023 · 1,532 citations*](https://md.research.elamur.ai/free/en/papers/gilardi-2023-chatgpt-outperforms-crowd-workers-text/)
7. [**Sequence-Level Knowledge Distillation**Yoon Kim, Alexander M. Rush*2016 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/kim-2016-sequence-level-knowledge-distillation/)
8. [**An Empirical Study of Smoothing Techniques for Language Modeling**Stanley F. Chen, Joshua Goodman*1996 · 1,209 citations*](https://md.research.elamur.ai/free/en/papers/chen-1996-empirical-study-smoothing-techniques-language/)
9. [**Predicting the Type and Target of Offensive Posts in Social Media**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-predicting-type-target-offensive-posts/)
10. [**Abstractive Sentence Summarization with Attentive Recurrent Neural Networks**Sumit Chopra, Michael Auli, Alexander M. Rush*2016 · 1,085 citations*](https://md.research.elamur.ai/free/en/papers/chopra-2016-abstractive-sentence-summarization-attentive-recurrent/)
11. [**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/)
12. [**Bottom-Up Abstractive Summarization**Sebastian Gehrmann, Yuntian Deng, Alexander Rush*2018 · 939 citations*](https://md.research.elamur.ai/free/en/papers/gehrmann-2018-bottom-up-abstractive-summarization/)
13. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 936 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
14. [**Extracting Training Data from Large Language Models**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 901 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
15. [**Challenges in Data-to-Document Generation**Sam Wiseman, Stuart M. Shieber, Alexander M. Rush*2017 · 846 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2017-challenges-data-document-generation/)
16. [**Findings of the 2019 Conference on Machine Translation (WMT19)**Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, et al.*2019 · 839 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2019-findings-2019-conference-machine-translation/)
17. [**ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission**Kexin Huang, Jaan Altosaar, Rajesh Ranganath*2019 · 788 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-clinicalbert-modeling-clinical-notes-predicting/)
18. [**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/)
19. [**Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations**Philippe Tillet, H. T. Kung, David Cox*2019 · 728 citations*](https://md.research.elamur.ai/free/en/papers/tillet-2019-triton-intermediate-language-compiler-tiled/)
20. [**GLTR: Statistical Detection and Visualization of Generated Text**Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush*2019 · 691 citations*](https://md.research.elamur.ai/free/en/papers/gehrmann-2019-gltr-statistical-detection-visualization-generated/)
21. [**A Critique and Improvement of an Evaluation Metric for Text Segmentation**Lev Pevzner, Marti A. Hearst*2002 · 594 citations*](https://md.research.elamur.ai/free/en/papers/pevzner-2002-critique-improvement-evaluation-metric-text/)
22. [**Benchmarking Aggression Identification in Social Media**Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, et al.*2018 · 507 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2018-benchmarking-aggression-identification-social-media/)
23. [**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/)
24. [**A Stochastic Finite-State Word-Segmentation Algorithm for Chinese**Richard W. Sproat, Chilin Shih, William Gale, et al.*1996 · 410 citations*](https://md.research.elamur.ai/free/en/papers/sproat-1996-stochastic-finite-state-word-segmentation/)
25. [**Synchronous Tree-Adjoining Grammars**Stuart M. Shieber, Yves Schabes*1990 · 404 citations*](https://md.research.elamur.ai/free/en/papers/shieber-1990-synchronous-tree-adjoining-grammars/)
26. [**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/)
27. [**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/)
28. [**Temporal Annotation in the Clinical Domain**William F. Styler IV, Steven Bethard, Sean Finan, et al.*2014 · 349 citations*](https://md.research.elamur.ai/free/en/papers/iv-2014-temporal-annotation-clinical-domain/)
29. [**AugGPT: Leveraging ChatGPT for Text Data Augmentation**Haixing Dai; Zhengliang Liu; Wenxiong Liao; Xiaoke Huang; Yihan Cao; Zihao Wu; Lin Zhao; Shaochen Xu; Wei Liu; Ninghao Liu; Sheng Li; Dajiang Zhu; Hongmin Cai; Lichao Sun; Quanzheng Li; Dinggang Shen; Tianming Liu;, Xiang Li*2023 · 302 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-auggpt-leveraging-chatgpt-text-data/)
30. [**Aligning Sentences in Bilingual Corpora Using Lexical Information**Stanley F. Chen*1993 · 299 citations*](https://md.research.elamur.ai/free/en/papers/chen-1993-aligning-sentences-bilingual-corpora-lexical/)
31. [**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/)
32. [**Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation**Sameer Pradhan, Xiaoqiang Luo, Marta Recasens, et al.*2014 · 284 citations*](https://md.research.elamur.ai/free/en/papers/pradhan-2014-scoring-coreference-partitions-predicted-mentions/)
33. [**Learning Neural Templates for Text Generation**Sam Wiseman, Stuart Shieber, Alexander Rush*2018 · 279 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2018-learning-neural-templates-text-generation/)
34. [**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/)
35. [**Learning Global Features for Coreference Resolution**Sam Wiseman, Alexander M. Rush, Stuart M. Shieber*2016 · 264 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2016-learning-global-features-coreference-resolution/)
36. [**SemEval-2016 Task 12: Clinical TempEval**Steven Bethard, Guergana Savova, Wei-Te Chen, et al.*2016 · 263 citations*](https://md.research.elamur.ai/free/en/papers/bethard-2016-semeval-2016-task-12-clinical/)
37. [**Findings of the VarDial Evaluation Campaign 2017**Marcos Zampieri, Shervin Malmasi, Nikola Ljubešić, et al.*2017 · 261 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2017-findings-vardial-evaluation-campaign/)
38. [**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/)
39. [**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/)
40. [**FOLIO: Natural Language Reasoning with First-Order Logic**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 217 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
41. [**Systematic Inequalities in Language Technology Performance across the World's Languages**Damian Blasi, Antonios Anastasopoulos, Graham Neubig*2022 · 214 citations*](https://md.research.elamur.ai/free/en/papers/blasi-2022-systematic-inequalities-language-technology-performance/)
42. [**Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution**Sam Wiseman, Alexander M. Rush, Stuart Shieber, et al.*2015 · 209 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2015-learning-anaphoricity-antecedent-ranking-features/)
43. [**LILA: A Unified Benchmark for Mathematical Reasoning**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/)
44. [**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/)
45. [**Probing What Different NLP Tasks Teach Machines about Function Word Comprehension**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 161 citations*](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/)
46. [**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/)
47. [**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/)
48. [**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/)
49. [**Arabic Diacritization Using Weighted Finite-State Transducers**Rani Nelken, Stuart M. Shieber*2005 · 116 citations*](https://md.research.elamur.ai/free/en/papers/nelken-2005-arabic-diacritization-weighted-finite-state/)
50. [**Certifying LLM Safety against Adversarial Prompting**Aounon Kumar, Chirag Agarwal, Suraj Srinivas, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2023-certifying-llm-safety-against-adversarial/)
51. [**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/)
52. [**An HMM Approach to Vowel Restoration in Arabic and Hebrew**Ya'akov Gal*2002 · 106 citations*](https://md.research.elamur.ai/free/en/papers/gal-2002-hmm-approach-vowel-restoration-arabic/)
53. [**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/)
54. [**The Computational Complexity of Avoiding Conversational Implicatures**Ehud Reiter*1990 · 73 citations*](https://md.research.elamur.ai/free/en/papers/reiter-1990-computational-complexity-avoiding-conversational-implicatures/)
55. [**Tokenization Is More Than Compression**Craig W Schmidt, Varshini Reddy, Haoran Zhang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/schmidt-2024-tokenization-is-more-than-compression/)
56. [**How well do NLI models capture verb veridicality?**Alexis Ross, Ellie Pavlick*2019 · 61 citations*](https://md.research.elamur.ai/free/en/papers/ross-2019-how-well-do-nli-models/)
57. [**Unsupervised Grammar Induction with Depth-bounded PCFG**Lifeng Jin, Finale Doshi-Velez, Timothy Miller, et al.*2018 · 55 citations*](https://md.research.elamur.ai/free/en/papers/jin-2018-unsupervised-grammar-induction-depth-bounded/)
58. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
59. [**Cross-document coreference: An approach to capturing coreference without context**Kristin Wright-Bettner, Martha Palmer, Guergana Savova, et al.*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/bettner-2019-cross-document-coreference-approach-capturing/)
60. [**Bimorphisms and synchronous grammars**Stuart M. Shieber*2014 · 5 citations*](https://md.research.elamur.ai/free/en/papers/shieber-2014-bimorphisms-synchronous-grammars/)
61. [**Subject to change: Agreement and case**Nofar Rimon, Tal Siloni*2026*](https://md.research.elamur.ai/free/en/papers/rimon-2026-subject-change-agreement-case/)
62. [**A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations**Timothy A. Miller, Gaby Dinh, David Harris, et al.*2026*](https://md.research.elamur.ai/free/en/papers/miller-2026-dataset-psychiatric-hospital-notes-temporal/)
63. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)
64. [**Synchronous Grammars and Transducers: Good News and Bad News**Stuart Shieber*2007*](https://md.research.elamur.ai/free/en/papers/shieber-2007-synchronous-grammars-transducers-good-news/)
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