---
title: "Who cites Jeffrey Pennington — page 2"
description: "6,292 papers of the Elamur Research library cite the works of Jeffrey Pennington, found in their bibliographies · page 2 of 6 · 101–200; the most cited…"
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# Who cites Jeffrey Pennington

6,358 citations of their works from 6,292 papers of our library (of the 140,884 whose bibliographies we read) · 557 with a page here, the most cited first · page 2 of 6 · 101–200 · [← Jeffrey Pennington](https://md.research.elamur.ai/free/en/authors/jeffrey-pennington/)

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101. [**Semi-supervised sequence tagging with bidirectional language models**Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, et al.*2017 · 786 citations*](https://md.research.elamur.ai/free/en/papers/peters-2017-semi-supervised-sequence-tagging-bidirectional/)
102. [**Relational Graph Attention Network for Aspect-based Sentiment Analysis**Kai Wang, Weizhou Shen, Yunyi Yang, et al.*2020 · 775 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/)
103. [**Ask Me Anything: Dynamic Memory Networks for Natural Language Processing**Ankit Kumar, Ozan Irsoy, Peter Ondruska, et al.*2015 · 775 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2015-ask-me-anything-dynamic-memory/)
104. [**Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths**Yan Xu, Lili Mou, Ge Li, et al.*2015 · 769 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-classifying-relations-long-short-term/)
105. [**On Measuring Social Biases in Sentence Encoders**Chandler May, Alex Wang, Shikha Bordia, et al.*2019 · 761 citations*](https://md.research.elamur.ai/free/en/papers/may-2019-measuring-social-biases-sentence-encoders/)
106. [**Learning Distributed Representations of Sentences from Unlabelled Data**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/)
107. [**EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks**Jason Wei, Kai Zou*2019 · 750 citations*](https://md.research.elamur.ai/free/en/papers/wei-2019-eda-easy-data-augmentation-techniques/)
108. [**Integrating Multimodal Information in Large Pretrained Transformers**Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, et al.*2020 · 742 citations*](https://md.research.elamur.ai/free/en/papers/rahman-2020-integrating-multimodal-information-large-pretrained/)
109. [**SentEval: An Evaluation Toolkit for Universal Sentence Representations**Alexis Conneau, Douwe Kiela*2018 · 739 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/)
110. [**A Novel Cascade Binary Tagging Framework for Relational Triple Extraction**Zhepei Wei, Jianlin Su, Yue Wang, et al.*2020 · 732 citations*](https://md.research.elamur.ai/free/en/papers/wei-2020-novel-cascade-binary-tagging-framework/)
111. [**IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
112. [**Adversarial Multi-task Learning for Text Classification**Pengfei Liu, Xipeng Qiu, Xuanjing Huang*2017 · 723 citations*](https://md.research.elamur.ai/free/en/papers/liu-2017-adversarial-multi-task-learning-text/)
113. [**Style Transfer in Text: Exploration and Evaluation**Zhenxin Fu, Xiaoye Tan, Nanyun Peng, et al.*2017 · 701 citations*](https://md.research.elamur.ai/free/en/papers/fu-2017-style-transfer-text-exploration-evaluation/)
114. [**Effective LSTMs for Target-Dependent Sentiment Classification**Duyu Tang, Bing Qin, Xiaocheng Feng, et al.*2016 · 693 citations*](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
115. [**Explain Yourself! Leveraging Language Models for Commonsense Reasoning**Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, et al.*2019 · 690 citations*](https://md.research.elamur.ai/free/en/papers/rajani-2019-explain-yourself-leveraging-language-models/)
116. [**A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task**Danqi Chen, Jason Bolton, Christopher D. Manning*2016 · 689 citations*](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/)
117. [**Bilingual Word Embeddings for Phrase-Based Machine Translation**Will Y. Zou, Richard Socher, Daniel Cer, et al.*2013 · 680 citations*](https://md.research.elamur.ai/free/en/papers/zou-2013-bilingual-word-embeddings-phrase-based/)
118. [**Well-Read Students Learn Better: On the Importance of Pre-training Compact Models**Iulia Turc, Ming-Wei Chang, Kenton Lee, et al.*2019 · 671 citations*](https://md.research.elamur.ai/free/en/papers/turc-2019-well-read-students-learn-better/)
119. [**TVQA: Localized, Compositional Video Question Answering**Jie Lei, Licheng Yu, Mohit Bansal, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/)
120. [**Evaluation methods for unsupervised word embeddings**Tobias Schnabel, Igor Labutov, David Mimno, et al.*2015 · 669 citations*](https://md.research.elamur.ai/free/en/papers/schnabel-2015-evaluation-methods-unsupervised-word-embeddings/)
121. [**context2vec: Learning Generic Context Embedding with Bidirectional LSTM**Oren Melamud, Jacob Goldberger, Ido Dagan*2016 · 666 citations*](https://md.research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/)
122. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 650 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/)
123. [**Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging**Nils Reimers, Iryna Gurevych*2017 · 648 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2017-reporting-score-distributions-makes-difference/)
124. [**FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 645 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/)
125. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 640 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
126. [**Learning Word Vectors for 157 Languages**Edouard Grave, Piotr Bojanowski, Prakhar Gupta, et al.*2018 · 640 citations*](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/)
127. [**Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond**Mikel Artetxe, Holger Schwenk*2018 · 627 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2018-massively-multilingual-sentence-embeddings-zero/)
128. [**Harnessing Deep Neural Networks with Logic Rules**Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al.*2016 · 619 citations*](https://md.research.elamur.ai/free/en/papers/hu-2016-harnessing-deep-neural-networks-logic/)
129. [**Advances in Pre-Training Distributed Word Representations**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 614 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/)
130. [**Linguistically-Informed Self-Attention for Semantic Role Labeling**Emma Strubell, Patrick Verga, Daniel Andor, et al.*2018 · 598 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2018-linguistically-informed-self-attention-semantic/)
131. [**Attention Guided Graph Convolutional Networks for Relation Extraction**Zhijiang Guo, Yan Zhang, Wei Lu*2019 · 597 citations*](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/)
132. [**Transformation Networks for Target-Oriented Sentiment Classification**Xin Li, Lidong Bing, Wai Lam, et al.*2018 · 594 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/)
133. [**Deep Semantic Role Labeling: What Works and What's Next**Luheng He, Kenton Lee, Mike Lewis, et al.*2017 · 589 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-deep-semantic-role-labeling-what/)
134. [**DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis**Christos Baziotis, Nikos Pelekis, Christos Doulkeridis*2017 · 587 citations*](https://md.research.elamur.ai/free/en/papers/baziotis-2017-datastories-semeval-2017-task-4/)
135. [**Dissecting Contextual Word Embeddings: Architecture and Representation**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 585 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/)
136. [**Measuring Bias in Contextualized Word Representations**Keita Kurita, Nidhi Vyas, Ayush Pareek, et al.*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/kurita-2019-measuring-bias-contextualized-word-representations/)
137. [**PAWS: Paraphrase Adversaries from Word Scrambling**Yuan Zhang, Jason Baldridge, Luheng He*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-paws-paraphrase-adversaries-word-scrambling/)
138. [**What do you learn from context? Probing for sentence structure in contextualized word representations**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 582 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
139. [**Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer**Sudha Rao, Joel Tetreault*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/rao-2018-dear-sir-or-madam-may/)
140. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
141. [**Dynamic Coattention Networks For Question Answering**Caiming Xiong, Victor Zhong, Richard Socher*2016 · 578 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/)
142. [**An efficient framework for learning sentence representations**Lajanugen Logeswaran, Honglak Lee*2018 · 573 citations*](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
143. [**Sentence Simplification with Deep Reinforcement Learning**Xingxing Zhang, Mirella Lapata*2017 · 567 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-sentence-simplification-deep-reinforcement-learning/)
144. [**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/)
145. [**Leveraging Pre-trained Checkpoints for Sequence Generation Tasks**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/)
146. [**Neural Legal Judgment Prediction in English**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 558 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/)
147. [**Simple and Effective Multi-Paragraph Reading Comprehension**Christopher Clark, Matt Gardner*2017 · 556 citations*](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/)
148. [**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/)
149. [**BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis**Hu Xu, Bing Liu, Lei Shu, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
150. [**TextBugger: Generating Adversarial Text Against Real-world Applications**Jinfeng Li, Shouling Ji, Tianyu Du, et al.*2018 · 541 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-textbugger-generating-adversarial-text-against/)
151. [**Machine Comprehension Using Match-LSTM and Answer Pointer**Shuohang Wang, Jing Jiang*2016 · 528 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-machine-comprehension-using-match-lstm/)
152. [**TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking**Yucheng Wang, Bowen Yu, Yueyang Zhang, et al.*2020 · 527 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-tplinker-single-stage-joint-extraction/)
153. [**Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters**Charlie Snell, Jaehoon Lee, Kelvin Xu, et al.*2024 · 524 citations*](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/)
154. [**The Natural Language Decathlon: Multitask Learning as Question Answering**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
155. [**How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/)
156. [**Breaking NLI Systems with Sentences that Require Simple Lexical Inferences**Max Glockner, Vered Shwartz, Yoav Goldberg*2018 · 517 citations*](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)
157. [**Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 514 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/)
158. [**DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 513 citations*](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/)
159. [**Crowdsourcing Multiple Choice Science Questions**Johannes Welbl, Nelson F. Liu, Matt Gardner*2017 · 513 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/)
160. [**Learning Gender-Neutral Word Embeddings**Jieyu Zhao, Yichao Zhou, Zeyu Li, et al.*2018 · 512 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/)
161. [**Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation**Xiao Liu, Zhunchen Luo, Heyan Huang*2018 · 509 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-jointly-multiple-events-extraction-attention/)
162. [**Gender Bias in Contextualized Word Embeddings**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
163. [**Inducing Crosslingual Distributed Representations of Words**Alexandre Klementiev, Ivan Titov, Binod Bhattarai*2012 · 501 citations*](https://md.research.elamur.ai/free/en/papers/klementiev-2012-inducing-crosslingual-distributed-representations-words/)
164. [**Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree**Kai Sun, Richong Zhang, Samuel Mensah, et al.*2019 · 497 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-aspect-level-sentiment-analysis-convolution/)
165. [**Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction**Hu Xu, Bing Liu, Lei Shu, et al.*2018 · 494 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-double-embeddings-cnn-based-sequence/)
166. [**Deep Visual-Semantic Alignments for Generating Image Descriptions**Andrej Karpathy, Li Fei-Fei*2014 · 493 citations*](https://md.research.elamur.ai/free/en/papers/karpathy-2014-deep-visual-semantic-alignments-generating/)
167. [**Learning Natural Language Inference with LSTM**Shuohang Wang, Jing Jiang*2016 · 491 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-learning-natural-language-inference-lstm/)
168. [**A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/)
169. [**Semi-Supervised Sequence Modeling with Cross-View Training**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 489 citations*](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/)
170. [**Learned in Translation: Contextualized Word Vectors**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 481 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
171. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
172. [**The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems**Ryan Lowe, Nissan Pow, Iulian Serban, et al.*2015 · 474 citations*](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/)
173. [**Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models**Minh-Thang Luong, Christopher D. Manning*2016 · 469 citations*](https://md.research.elamur.ai/free/en/papers/luong-2016-achieving-open-vocabulary-neural-machine/)
174. [**SemEval-2018 Task 3: Irony Detection in English Tweets**Cynthia Van Hee, Els Lefever, Véronique Hoste*2018 · 467 citations*](https://md.research.elamur.ai/free/en/papers/hee-2018-semeval-2018-task-3-irony/)
175. [**Learning to Generate Reviews and Discovering Sentiment**Alec Radford, Rafal Jozefowicz, Ilya Sutskever*2017 · 467 citations*](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
176. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT**Shijie Wu, Mark Dredze*2019 · 466 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
177. [**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/)
178. [**Bilingual Word Representations with Monolingual Quality in Mind**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 453 citations*](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/)
179. [**Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model**Yitao Cai, Huiyu Cai, Xiaojun Wan*2019 · 450 citations*](https://md.research.elamur.ai/free/en/papers/cai-2019-multi-modal-sarcasm-detection-twitter/)
180. [**Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks**Hua He, Kevin Gimpel, Jimmy Lin*2015 · 447 citations*](https://md.research.elamur.ai/free/en/papers/he-2015-multi-perspective-sentence-similarity-modeling/)
181. [**HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization**Xingxing Zhang, Furu Wei, Ming Zhou*2019 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-hibert-document-level-pre-training/)
182. [**Neural Document Summarization by Jointly Learning to Score and Select Sentences**Qingyu Zhou, Nan Yang, Furu Wei, et al.*2018 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2018-neural-document-summarization-jointly-learning/)
183. [**OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs**Seungwhan Moon, Pararth Shah, Anuj Kumar, et al.*2019 · 444 citations*](https://md.research.elamur.ai/free/en/papers/moon-2019-opendialkg-explainable-conversational-reasoning-attention/)
184. [**Pretrained Transformers Improve Out-of-Distribution Robustness**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 442 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/)
185. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
186. [**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/)
187. [**oLMpics — On what Language Model Pre-training Captures**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 432 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/)
188. [**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/)
189. [**Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora**William L. Hamilton, Kevin Clark, Jure Leskovec, et al.*2016 · 431 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/)
190. [**Experience Grounds Language**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 429 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/)
191. [**Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation**Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, et al.*2016 · 427 citations*](https://md.research.elamur.ai/free/en/papers/yamada-2016-joint-learning-embedding-words-entities/)
192. [**Natural Language Inference by Tree-Based Convolution and Heuristic Matching**Lili Mou, Rui Men, Ge Li, et al.*2016 · 425 citations*](https://md.research.elamur.ai/free/en/papers/mou-2016-natural-language-inference-tree-based/)
193. [**Diffusion-LM Improves Controllable Text Generation**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 421 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
194. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 420 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
195. [**Empower Sequence Labeling with Task-Aware Neural Language Model**Liyuan Liu, Jingbo Shang, Frank F. Xu, et al.*2017 · 419 citations*](https://md.research.elamur.ai/free/en/papers/liu-2017-empower-sequence-labeling-task-aware/)
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