---
title: "Who cites Pointer Sentinel Mixture Models"
description: "1,242 papers of the Elamur Research library cite «Pointer Sentinel Mixture Models», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/merity-2016-pointer-sentinel-mixture-models/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Pointer Sentinel Mixture Models](https://md.research.elamur.ai/free/en/papers/merity-2016-pointer-sentinel-mixture-models/) / cited by

# Who cites «Pointer Sentinel Mixture Models»

1,242 papers of our library cite it (of the 140,884 whose bibliographies we read) · 77 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/merity-2016-pointer-sentinel-mixture-models/)

1. [**Neural Architecture Search with Reinforcement Learning**Barret Zoph, Quoc V. Le*2016 · 4,331 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
2. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,780 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
3. [**Language Models as Knowledge Bases?**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,977 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/)
4. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
5. [**Emergent Abilities of Large Language Models**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,215 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
6. [**Toolformer: Language Models Can Teach Themselves to Use Tools**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,020 citations*](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/)
7. [**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/)
8. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
9. [**Retrieval-Augmented Generation for Large Language Models: A Survey**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,647 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
10. [**A Deep Reinforced Model for Abstractive Summarization**Romain Paulus, Caiming Xiong, Richard Socher*2017 · 1,626 citations*](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/)
11. [**Efficient Transformers: A Survey**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/)
12. [**Language Modeling with Gated Convolutional Networks**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,305 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
13. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
14. [**Training Compute-Optimal Large Language Models**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-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. [**Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations**Sosuke Kobayashi*2018 · 804 citations*](https://md.research.elamur.ai/free/en/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/)
17. [**Efficient Content-Based Sparse Attention with Routing Transformers**Aurko Roy, Mohammad Saffar, Ashish Vaswani, et al.*2020 · 660 citations*](https://md.research.elamur.ai/free/en/papers/roy-2020-efficient-content-based-sparse-attention/)
18. [**Regularizing and Optimizing LSTM Language Models**Stephen Merity, Nitish Shirish Keskar, Richard Socher*2017 · 605 citations*](https://md.research.elamur.ai/free/en/papers/merity-2017-regularizing-optimizing-lstm-language-models/)
19. [**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/)
20. [**Reducing Transformer Depth on Demand with Structured Dropout**Angela Fan, Edouard Grave, Armand Joulin*2019 · 546 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
21. [**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/)
22. [**Neural Text Generation with Unlikelihood Training**Sean Welleck, Ilia Kulikov, Stephen Roller, et al.*2019 · 498 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/)
23. [**Regularizing Neural Networks by Penalizing Confident Output Distributions**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 482 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
24. [**LLM-Pruner: On the Structural Pruning of Large Language Models**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 447 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/)
25. [**Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems**Andrea Madotto, Chien-Sheng Wu, Pascale Fung*2018 · 401 citations*](https://md.research.elamur.ai/free/en/papers/madotto-2018-mem2seq-effectively-incorporating-knowledge-bases/)
26. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 399 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/)
27. [**Gender Bias in Neural Natural Language Processing**Kaiji Lu, Piotr Mardziel, Fangjing Wu, et al.*2018 · 394 citations*](https://md.research.elamur.ai/free/en/papers/lu-2018-gender-bias-neural-natural-language/)
28. [**Retrieval Augmentation Reduces Hallucination in Conversation**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 382 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
29. [**A Survey on Model Compression for Large Language Models**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/)
30. [**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/)
31. [**Identifying and Reducing Gender Bias in Word-Level Language Models**Shikha Bordia, Samuel R. Bowman*2019 · 363 citations*](https://md.research.elamur.ai/free/en/papers/bordia-2019-identifying-reducing-gender-bias-word/)
32. [**Augmented Language Models: a Survey**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 362 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
33. [**Internet-Augmented Dialogue Generation**Mojtaba Komeili, Kurt Shuster, Jason Weston*2022 · 349 citations*](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/)
34. [**Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space**Mor Geva, Avi Caciularu, Kevin Ro Wang, et al.*2022 · 342 citations*](https://md.research.elamur.ai/free/en/papers/geva-2022-transformer-feed-forward-layers-build/)
35. [**Compressive Transformers for Long-Range Sequence Modelling**Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, et al.*2019 · 320 citations*](https://md.research.elamur.ai/free/en/papers/rae-2019-compressive-transformers-long-range-sequence/)
36. [**Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense**Kalpesh Krishna, Yixiao Song, Marzena Karpinska, et al.*2023 · 304 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2023-paraphrasing-evades-detectors-ai-generated/)
37. [**ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 301 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/)
38. [**KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization**Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, et al.*2024 · 293 citations*](https://md.research.elamur.ai/free/en/papers/hooper-2024-kvquant-towards-10-million-context/)
39. [**AlignScore: Evaluating Factual Consistency with A Unified Alignment Function**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/)
40. [**LLM-QAT: Data-Free Quantization Aware Training for Large Language Models**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 275 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/)
41. [**Towards Understanding and Mitigating Social Biases in Language Models**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 263 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
42. [**Towards Debiasing Sentence Representations**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/)
43. [**QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs**Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, et al.*2024 · 252 citations*](https://md.research.elamur.ai/free/en/papers/ashkboos-2024-quarot-outlier-free-4-bit/)
44. [**Personality Traits in Large Language Models**Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.*2023 · 251 citations*](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/)
45. [**BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model**Alex Wang, Kyunghyun Cho*2019 · 249 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-bert-has-mouth-must-speak/)
46. [**Contrastive Decoding: Open-ended Text Generation as Optimization**Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al.*2022 · 246 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/)
47. [**Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset**Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, et al.*2019 · 244 citations*](https://md.research.elamur.ai/free/en/papers/byrne-2019-taskmaster-1-realistic-diverse-dialog/)
48. [**An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/)
49. [**LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders**Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.*2024 · 209 citations*](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/)
50. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
51. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
52. [**OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models**Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.*2023 · 193 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/)
53. [**Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/)
54. [**Knowledge Conflicts for LLMs: A Survey**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 179 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)
55. [**Representation Degeneration Problem in Training Natural Language Generation Models**Jun Gao, Di He, Xu Tan, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/gao-2019-representation-degeneration-problem-training-natural/)
56. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
57. [**SqueezeLLM: Dense-and-Sparse Quantization**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
58. [**Quark: Controllable Text Generation with Reinforced Unlearning**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 164 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/)
59. [**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/)
60. [**Locally Typical Sampling**Clara Meister, Tiago Pimentel, Gian Wiher, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/)
61. [**Simple and Effective Masked Diffusion Language Models**Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, et al.*2024 · 134 citations*](https://md.research.elamur.ai/free/en/papers/sahoo-2024-simple-effective-masked-diffusion-language/)
62. [**Extracting Latent Steering Vectors from Pretrained Language Models**Nishant Subramani, Nivedita Suresh, Matthew Peters*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/)
63. [**RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation**Fangyuan Xu, Weijia Shi, Eunsol Choi*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/)
64. [**D4: Improving LLM Pretraining via Document De-Duplication and Diversification**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/)
65. [**Evaluating prose style transfer with the Bible**Keith Carlson, Allen Riddell, Daniel Rockmore*2017 · 77 citations*](https://md.research.elamur.ai/free/en/papers/carlson-2017-evaluating-prose-style-transfer-bible/)
66. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
67. [**Understanding Transformer Memorization Recall Through Idioms**Adi Haviv, Ido Cohen, Jacob Gidron, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/haviv-2023-understanding-transformer-memorization-recall-through/)
68. [**CxGBERT: BERT meets Construction Grammar**Harish Tayyar Madabushi, Laurence Romain, Dagmar Divjak, et al.*2020 · 43 citations*](https://md.research.elamur.ai/free/en/papers/madabushi-2020-cxgbert-bert-meets-construction-grammar/)
69. [**The AraGenEval Shared Task on Arabic Authorship Style Transfer and AI Generated Text Detection**Shadi Abudalfa, Saad Ezzini, Ahmed Abdelali, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/abudalfa-2025-arageneval-shared-task-arabic-authorship/)
70. [**Language acquisition: do children and language models follow similar learning stages?**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
71. [**A closer look at the performance of neural language models on reflexive anaphor licensing**Jennifer Hu, Sherry Yong Chen, Roger Levy*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-closer-look-performance-neural-language/)
72. [**BLiMP: The Benchmark of Linguistic Minimal Pairs for English**Alex Warstadt, Alicia Parrish, Haokun Liu, et al.*2020 · 19 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2020-blimp-benchmark-linguistic-minimal-pairs/)
73. [**The Third Multilingual Surface Realisation Shared Task (SR'20): Overview and Evaluation Results**Simon Mille, Anya Belz, Bernd Bohnet, et al.*2020 · 18 citations*](https://md.research.elamur.ai/free/en/papers/mille-2020-third-multilingual-surface-realisation-shared/)
74. [**Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models**Giulia Rambelli, Emmanuele Chersoni, Marco S. G. Senaldi, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/rambelli-2023-are-frequent-phrases-directly-retrieved/)
75. [**Entropy-aware Masking for Masked Language Modeling**Gokul Srinivasagan, Kai Hartung, Munir Georges*2026*](https://md.research.elamur.ai/free/en/papers/srinivasagan-2026-entropy-aware-masking-masked-language/)
76. [**Low-Rank Compression of Language Models via Differentiable Rank Selection**Sidhant Sundrani, Francesco Tudisco, Pasquale Minervini*2026*](https://md.research.elamur.ai/free/en/papers/sundrani-2026-low-rank-compression-language-models/)
77. [**Rethinking the Relationship between the Power Law and Hierarchical Structures**Kai Nakaishi, Ryo Yoshida, Kohei Kajikawa, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nakaishi-2026-rethinking-relationship-between-power-law/)

1,165 more papers of our library cite it; they have no page here yet.

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