---
title: "Microsoft — works"
description: "193 works by researchers of Microsoft in our library · page 1 of 2, the most cited first."
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# Works of Microsoft

193 works · page 1 of 2 · the most cited first · [← Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/)

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1. [**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/)
2. [**Microsoft COCO: Common Objects in Context**Tsung-Yi Lin, Michael Maire, Serge Belongie, et al.*2014 · 4,529 citations*](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
3. [**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/)
4. [**CodeBERT: A Pre-Trained Model for Programming and Natural Languages**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,954 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/)
5. [**A Diversity-Promoting Objective Function for Neural Conversation Models**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 2,925 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/)
6. [**Learning to Rank using Gradient Descent**Chris Burges, Tal Shaked, Erin Renshaw, et al.*2005 · 2,896 citations*](https://md.research.elamur.ai/free/en/papers/burges-2005-learning-rank-using-gradient/)
7. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,636 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
8. [**Improved Baselines with Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,557 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/)
9. [**Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Qingyang Wu, et al.*2023 · 2,487 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/)
10. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,389 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
11. [**UNITER: UNiversal Image-TExt Representation Learning**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,344 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/)
12. [**Automatically Constructing a Corpus of Sentential Paraphrases**William B. Dolan, Chris Brockett*2005 · 1,839 citations*](https://md.research.elamur.ai/free/en/papers/dolan-2005-automatically-constructing-corpus-sentential-paraphrases/)
13. [**Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks**Xiujun Li, Xi Yin, Chunyuan Li, et al.*2020 · 1,827 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/)
14. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,800 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
15. [**Language (Technology) is Power: A Critical Survey of “Bias” in NLP**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,700 citations*](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/)
16. [**Multi-Task Deep Neural Networks for Natural Language Understanding**Xiaodong Liu, Pengcheng He, Weizhu Chen, et al.*2019 · 1,501 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-multi-task-deep-neural-networks/)
17. [**The State and Fate of Linguistic Diversity and Inclusion in the NLP World**Pratik Joshi, Sebastin Santy, Amar Budhiraja, et al.*2020 · 1,492 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2020-state-fate-linguistic-diversity-inclusion/)
18. [**Deep Reinforcement Learning for Dialogue Generation**Jiwei Li, Will Monroe, Alan Ritter, et al.*2016 · 1,479 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-deep-reinforcement-learning-dialogue-generation/)
19. [**Unified Language Model Pre-training for Natural Language Understanding and Generation**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,389 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
20. [**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/)
21. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
22. [**What Makes Good In-Context Examples for GPT-3?**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,235 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
23. [**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/)
24. [**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/)
25. [**WikiQA: A Challenge Dataset for Open-Domain Question Answering**Yi Yang, Wen-tau Yih, Christopher Meek*2015 · 1,117 citations*](https://md.research.elamur.ai/free/en/papers/yang-2015-wikiqa-challenge-dataset-open-domain/)
26. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,008 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
27. [**MPNet: Masked and Permuted Pre-training for Language Understanding**Kaitao Song, Xu Tan, Tao Qin, et al.*2020 · 944 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-mpnet-masked-permuted-pre-training/)
28. [**Patient Knowledge Distillation for BERT Model Compression**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 939 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/)
29. [**Findings of the 2016 Conference on Machine Translation**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2016 · 936 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/)
30. [**Collecting Highly Parallel Data for Paraphrase Evaluation**David Chen, William Dolan*2011 · 926 citations*](https://md.research.elamur.ai/free/en/papers/chen-2011-collecting-highly-parallel-data-paraphrase/)
31. [**Representing Text for Joint Embedding of Text and Knowledge Bases**Kristina Toutanova, Danqi Chen, Patrick Pantel, et al.*2015 · 904 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2015-representing-text-joint-embedding-text/)
32. [**Are NLP Models really able to Solve Simple Math Word Problems?**Arkil Patel, Satwik Bhattamishra, Navin Goyal*2021 · 882 citations*](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/)
33. [**MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text**Matthew Richardson, Christopher J.C. Burges, Erin Renshaw*2013 · 873 citations*](https://md.research.elamur.ai/free/en/papers/richardson-2013-mctest-challenge-dataset-open-domain/)
34. [**Intelligent Selection of Language Model Training Data**Robert C. Moore, William Lewis*2010 · 848 citations*](https://md.research.elamur.ai/free/en/papers/moore-2010-intelligent-selection-language-model-training/)
35. [**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/)
36. [**Scaling to Very Very Large Corpora for Natural Language Disambiguation**Michele Banko, Eric Brill*2001 · 785 citations*](https://md.research.elamur.ai/free/en/papers/banko-2001-scaling-very-very-large-corpora/)
37. [**Domain Adaptation via Pseudo In-Domain Data Selection**Amittai Axelrod, Xiaodong He, Jianfeng Gao*2011 · 773 citations*](https://md.research.elamur.ai/free/en/papers/axelrod-2011-domain-adaptation-pseudo-domain-data/)
38. [**Findings of the 2018 Conference on Machine Translation (WMT18)**Ondřej Bojar, Christian Federmann, Mark Fishel, et al.*2018 · 747 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2018-findings-2018-conference-machine-translation/)
39. [**CIDEr: Consensus-based Image Description Evaluation**Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh*2014 · 733 citations*](https://md.research.elamur.ai/free/en/papers/vedantam-2014-cider-consensus-based-image-description/)
40. [**Data-Driven Response Generation in Social Media**Alan Ritter, Colin Cherry, William B. Dolan*2011 · 692 citations*](https://md.research.elamur.ai/free/en/papers/ritter-2011-data-driven-response-generation-social/)
41. [**Findings of the 2017 Conference on Machine Translation (WMT17)**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2017 · 685 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2017-findings-2017-conference-machine-translation/)
42. [**CORD-19: The COVID-19 Open Research Dataset**Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, et al.*2020 · 685 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-cord-19-covid-19-open/)
43. [**DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation**Yizhe Zhang, Siqi Sun, Michel Galley, et al.*2019 · 684 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-dialogpt-large-scale-generative-pre/)
44. [**Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval**Lee Xiong, Chenyan Xiong, Ye Li, et al.*2020 · 681 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2020-approximate-nearest-neighbor-negative-contrastive/)
45. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 662 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/)
46. [**An Improved Error Model for Noisy Channel Spelling Correction**Eric Brill, Robert C. Moore*2000 · 661 citations*](https://md.research.elamur.ai/free/en/papers/brill-2000-improved-error-model-noisy-channel/)
47. [**Instruction Tuning with GPT-4**Baolin Peng, Chunyuan Li, Pengcheng He, et al.*2023 · 651 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-instruction-tuning-gpt-4/)
48. [**Exploring Content Models for Multi-Document Summarization**Aria Haghighi, Lucy Vanderwende*2009 · 630 citations*](https://md.research.elamur.ai/free/en/papers/haghighi-2009-exploring-content-models-multi-document/)
49. [**LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding**Yang Xu, Yiheng Xu, Tengchao Lv, et al.*2021 · 623 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/)
50. [**AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation**Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.*2023 · 584 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/)
51. [**MIND: A Large-scale Dataset for News Recommendation**Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, et al.*2020 · 562 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-mind-large-scale-dataset-news/)
52. [**Unsupervised Modeling of Twitter Conversations**Alan Ritter, Colin Cherry, Bill Dolan*2010 · 557 citations*](https://md.research.elamur.ai/free/en/papers/ritter-2010-unsupervised-modeling-twitter-conversations/)
53. [**Cross-Sentence N-ary Relation Extraction with Graph LSTMs**Nanyun Peng, Hoifung Poon, Chris Quirk, et al.*2017 · 553 citations*](https://md.research.elamur.ai/free/en/papers/peng-2017-cross-sentence-n-ary-relation/)
54. [**Dependency Treelet Translation: Syntactically Informed Phrasal SMT**Chris Quirk, Arul Menezes, Colin Cherry*2005 · 549 citations*](https://md.research.elamur.ai/free/en/papers/quirk-2005-dependency-treelet-translation-syntactically-informed/)
55. [**The Importance of Syntactic Parsing and Inference in Semantic Role Labeling**Vasin Punyakanok, Dan Roth, Wen-tau Yih*2008 · 538 citations*](https://md.research.elamur.ai/free/en/papers/punyakanok-2008-importance-syntactic-parsing-inference-semantic/)
56. [**The Second Conversational Intelligence Challenge (ConvAI2)**Emily Dinan, Varvara Logacheva, Valentin Malykh, et al.*2019 · 525 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
57. [**Marian: Fast Neural Machine Translation in C++**Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, et al.*2018 · 506 citations*](https://md.research.elamur.ai/free/en/papers/junczys-dowmunt-2018-marian-fast-neural-machine-translation/)
58. [**Phi-4 Technical Report**Marah Abdin, Jyoti Aneja, Harkirat Behl, et al.*2024 · 489 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-4-technical-report/)
59. [**Semantic Parsing for Single-Relation Question Answering**Wen-tau Yih, Xiaodong He, Christopher Meek*2014 · 483 citations*](https://md.research.elamur.ai/free/en/papers/yih-2014-semantic-parsing-single-relation-question/)
60. [**Large Language Models: A Survey**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 467 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
61. [**Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval**Xiaodong Liu, Jianfeng Gao, Xiaodong He, et al.*2015 · 463 citations*](https://md.research.elamur.ai/free/en/papers/liu-2015-representation-learning-multi-task-deep/)
62. [**Findings of the 2015 Workshop on Statistical Machine Translation**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2015 · 461 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2015-findings-2015-workshop-statistical-machine/)
63. [**A Persona-Based Neural Conversation Model**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 456 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-persona-based-neural-conversation-model/)
64. [**ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training**Weizhen Qi, Yu Yan, Yeyun Gong, et al.*2020 · 451 citations*](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/)
65. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 443 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
66. [**Arabic Dialect Identification**Omar F. Zaidan, Chris Callison-Burch*2014 · 436 citations*](https://md.research.elamur.ai/free/en/papers/zaidan-2014-arabic-dialect-identification/)
67. [**Improving Text Embeddings with Large Language Models**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 433 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/)
68. [**Deep Communicating Agents for Abstractive Summarization**Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, et al.*2018 · 431 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/)
69. [**Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection**Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al.*2023 · 430 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-grounding-dino-marrying-dino-grounded/)
70. [**CLPsych 2015 Shared Task: Depression and PTSD on Twitter**Glen Coppersmith, Mark Dredze, Craig Harman, et al.*2015 · 410 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2015-clpsych-2015-shared-task-depression/)
71. [**Findings of the 2020 Conference on Machine Translation (WMT20)**Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, et al.*2020 · 401 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2020-findings-2020-conference-machine-translation/)
72. [**QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization**Ming Zhong, Da Yin, Tao Yu, et al.*2021 · 397 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2021-qmsum-new-benchmark-query-based/)
73. [**Query Rewriting in Retrieval-Augmented Large Language Models**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/)
74. [**Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms**Dinghan Shen, Guoyin Wang, Wenlin Wang, et al.*2018 · 383 citations*](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/)
75. [**Discourse-Aware Neural Extractive Text Summarization**Jiacheng Xu, Zhe Gan, Yu Cheng, et al.*2019 · 382 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/)
76. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 381 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
77. [**Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks**Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, et al.*2018 · 376 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-paragraph-level-neural-question-generation/)
78. [**Editing Models with Task Arithmetic**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 374 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
79. [**Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology**Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, et al.*2019 · 371 citations*](https://md.research.elamur.ai/free/en/papers/zmigrod-2019-counterfactual-data-augmentation-mitigating-gender/)
80. [**Question Generation for Question Answering**Nan Duan, Duyu Tang, Peng Chen, et al.*2017 · 370 citations*](https://md.research.elamur.ai/free/en/papers/duan-2017-question-generation-question-answering/)
81. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 366 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
82. [**Orca: Progressive Learning from Complex Explanation Traces of GPT-4**Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.*2023 · 357 citations*](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/)
83. [**Universal Neural Machine Translation for Extremely Low Resource Languages**Jiatao Gu, Hany Hassan, Jacob Devlin, et al.*2018 · 356 citations*](https://md.research.elamur.ai/free/en/papers/gu-2018-universal-neural-machine-translation-extremely/)
84. [**Query2doc: Query Expansion with Large Language Models**Liang Wang, Nan Yang, Furu Wei*2023 · 353 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-query2doc-query-expansion-large-language/)
85. [**Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access**Bhuwan Dhingra, Lihong Li, Xiujun Li, et al.*2017 · 347 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2017-end-end-reinforcement-learning-dialogue/)
86. [**Multilingual E5 Text Embeddings: A Technical Report**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 343 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-multilingual-e5-text-embeddings-technical/)
87. [**Generating Natural Questions About an Image**Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, et al.*2016 · 339 citations*](https://md.research.elamur.ai/free/en/papers/mostafazadeh-2016-generating-natural-questions-about-image/)
88. [**LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models**Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al.*2023 · 335 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-llmlingua-compressing-prompts-accelerated-inference/)
89. [**Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers**Chengyi Wang, Sanyuan Chen, Yu Wu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-neural-codec-language-models-zero/)
90. [**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/)
91. [**GraphCodeBERT: Pre-training Code Representations with Data Flow**Daya Guo, Shuo Ren, Shuai Lu, et al.*2021 · 328 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/)
92. [**MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities**Weihao Yu, Zhengyuan Yang, Linjie Li, et al.*2023 · 328 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/)
93. [**Search-based Neural Structured Learning for Sequential Question Answering**Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang*2017 · 327 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2017-search-based-neural-structured-learning/)
94. [**Textbooks Are All You Need II: phi-1.5 technical report**Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 327 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-textbooks-all-need-ii-phi/)
95. [**Correcting ESL Errors Using Phrasal SMT Techniques**Chris Brockett, William B. Dolan, Michael Gamon*2006 · 319 citations*](https://md.research.elamur.ai/free/en/papers/brockett-2006-correcting-esl-errors-phrasal-smt/)
96. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
97. [**A Neural Network Approach to Context-Sensitive Generation of Conversational Responses**Alessandro Sordoni, Michel Galley, Michael Auli, et al.*2015 · 307 citations*](https://md.research.elamur.ai/free/en/papers/sordoni-2015-neural-network-approach-context-sensitive/)
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