---
title: "Who cites Geoffrey E. Hinton — page 6 — 10,217 citing papers"
description: "10,217 papers of the Elamur Research library cite the works of Geoffrey E. Among them: Learning a SAT Solver from Single-Bit Supervision…"
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# Who cites Geoffrey E. Hinton

11,868 citations of their works from 10,217 papers of our library (of the 149,439 whose bibliographies we read) · 1,205 with a page here, the most cited first · page 6 of 13 · 501–600 · [← Geoffrey E. Hinton](https://md.research.elamur.ai/free/en/authors/geoffrey-e-hinton/)

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501. [**Learning a SAT Solver from Single-Bit Supervision arXiv:1802.03685**Daniel Selsam, Matthew Lamm, Benedikt Bünz, et al.*2018 · 232 citations*](https://md.research.elamur.ai/free/en/papers/selsam-2018-learning-sat-solver-single-bit/)
502. [**Concrete Problems in AI Safety arXiv:1606.06565**Dario Amodei, Chris Olah, Jacob Steinhardt, et al.*2016 · 232 citations*](https://md.research.elamur.ai/free/en/papers/amodei-2016-concrete-problems-ai-safety/)
503. **[Understanding the Difficulty of Training Transformers](https://md.research.elamur.ai/free/en/papers/liu-2020-understanding-difficulty-training-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyuan Liu, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 231 citations*
504. [**A Diagram Is Worth A Dozen Images arXiv:1603.07396**Aniruddha Kembhavi, Mike Salvato, Eric Kolve, et al.*2016 · 231 citations*](https://md.research.elamur.ai/free/en/papers/kembhavi-2016-diagram-worth-dozen-images/)
505. **[Cross-topic Argument Mining from Heterogeneous Sources](https://md.research.elamur.ai/free/en/papers/stab-2018-cross-topic-argument-mining-heterogeneous/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Christian Stab, Tristan Miller, Benjamin Schiller, et al.*2018 · 229 citations*
506. **[Strong Baselines for Neural Semi-Supervised Learning under Domain Shift](https://md.research.elamur.ai/free/en/papers/ruder-2018-strong-baselines-neural-semi-supervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sebastian Ruder, Barbara Plank*2018 · 229 citations*
507. [**Progress measures for grokking via mechanistic interpretability arXiv:2301.05217**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 228 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/)
508. **[Automatically Labeled Data Generation for Large Scale Event Extraction](https://md.research.elamur.ai/free/en/papers/chen-2017-automatically-labeled-data-generation-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yubo Chen, Shulin Liu, Xiang Zhang, et al.*2017 · 228 citations*
509. **[The University of Edinburgh's Neural MT Systems for WMT17](https://md.research.elamur.ai/free/en/papers/sennrich-2017-university-edinburgh-s-neural-mt/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Rico Sennrich, Alexandra Birch, Anna Currey, et al.*2017 · 228 citations*
510. [**Scalable agent alignment via reward modeling: a research direction arXiv:1811.07871**Jan Leike, David Krueger, Tom Everitt, et al.*2018 · 227 citations*](https://md.research.elamur.ai/free/en/papers/leike-2018-scalable-agent-alignment-reward-modeling/)
511. **[Towards String-To-Tree Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/aharoni-2017-string-tree-neural-machine-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roee Aharoni, Yoav Goldberg*2017 · 226 citations*
512. [**ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving arXiv:2309.17452**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 225 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-tora-tool-integrated-reasoning-agent/)
513. **[DataComp: In search of the next generation of multimodal datasets](https://md.research.elamur.ai/free/en/papers/gadre-2023-datacomp-search-next-generation-multimodal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.*2023 · 224 citations*
514. **[Beyond BLEU: Training Neural Machine Translation with Semantic Similarity](https://md.research.elamur.ai/free/en/papers/wieting-2019-beyond-bleu-training-neural-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, et al.*2019 · 223 citations*
515. **[MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection](https://md.research.elamur.ai/free/en/papers/zarrella-2016-mitre-semeval-2016-task-6/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Guido Zarrella, Amy Marsh*2016 · 223 citations*
516. **[Unsupervised Deep Embedding for Clustering Analysis](https://md.research.elamur.ai/free/en/papers/xie-2016-unsupervised-deep-embedding-clustering-analysis/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Junyuan Xie, Ross Girshick, Ali Farhadi*2016 · 222 citations*
517. **[Structured Pruning Learns Compact and Accurate Models](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 221 citations*
518. **[Few-Shot Charge Prediction with Discriminative Legal Attributes](https://md.research.elamur.ai/free/en/papers/hu-2018-few-shot-charge-prediction-discriminative/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zikun Hu, Xiang Li, Cunchao Tu, et al.*2018 · 221 citations*
519. **[Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 219 citations*
520. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch arXiv:2311.03099**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
521. **[Probing the Need for Visual Context in Multimodal Machine Translation](https://md.research.elamur.ai/free/en/papers/caglayan-2019-probing-need-visual-context-multimodal/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ozan Caglayan, Pranava Madhyastha, Lucia Specia, et al.*2019 · 217 citations*
522. **[Learning to Parse and Translate Improves Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/eriguchi-2017-learning-parse-translate-improves-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho*2017 · 217 citations*
523. **[Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Matt Deitke, Christopher Clark, Sangho Lee, et al.*2024 · 216 citations*
524. **[Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking](https://md.research.elamur.ai/free/en/papers/wen-2015-stochastic-language-generation-dialogue-recurrent/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Tsung-Hsien Wen, Milica Gašić, Dongho Kim, et al.*2015 · 216 citations*
525. **[You Impress Me: Dialogue Generation via Mutual Persona Perception](https://md.research.elamur.ai/free/en/papers/liu-2020-you-impress-me-dialogue-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qian Liu, Yihong Chen, Bei Chen, et al.*2020 · 215 citations*
526. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
527. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 213 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
528. **[TernaryBERT: Distillation-aware Ultra-low Bit BERT](https://md.research.elamur.ai/free/en/papers/zhang-2020-ternarybert-distillation-aware-ultra-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Zhang, Lu Hou, Yichun Yin, et al.*2020 · 213 citations*
529. [**Compositional Attention Networks for Machine Reasoning arXiv:1803.03067**Drew A. Hudson, Christopher D. Manning*2018 · 213 citations*](https://md.research.elamur.ai/free/en/papers/hudson-2018-compositional-attention-networks-machine-reasoning/)
530. [**Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation arXiv:2401.08417**Haoran Xu, Amr Sharaf, Yunmo Chen, et al.*2024 · 212 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/)
531. **[Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 212 citations*
532. **[An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*
533. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
534. [**EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty arXiv:2401.15077**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 208 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-speculative-sampling-requires-rethinking/)
535. **[On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nouha Dziri, Sivan Milton, Mo Yu, et al.*2022 · 208 citations*
536. **[Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Matena, Colin Raffel*2021 · 208 citations*
537. **[Entities as Experts: Sparse Memory Access with Entity Supervision](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 208 citations*
538. **[Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://md.research.elamur.ai/free/en/papers/wang-2021-improving-named-entity-recognition-external/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Wang, Yong Jiang, Nguyen Bach, et al.*2021 · 206 citations*
539. [**Efficient softmax approximation for GPUs arXiv:1609.04309**Edouard Grave, Armand Joulin, Moustapha Cisse, et al.*2016 · 205 citations*](https://md.research.elamur.ai/free/en/papers/grave-2016-efficient-softmax-approximation-gpus/)
540. **[DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/valipour-2023-dylora-parameter-efficient-tuning-pre/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, et al.*2023 · 203 citations*
541. [**Mesh-TensorFlow: Deep Learning for Supercomputers arXiv:1811.02084**Noam Shazeer, Youlong Cheng, Niki Parmar, et al.*2018 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shazeer-2018-mesh-tensorflow-deep-learning-supercomputers/)
542. [**Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs arXiv:2310.01801**Suyu Ge, Yunan Zhang, Liyuan Liu, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-model-tells-discard-adaptive-kv/)
543. **[Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2020-structured-pruning-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ziheng Wang, Jeremy Wohlwend, Tao Lei*2020 · 202 citations*
544. **[Compound Probabilistic Context-Free Grammars for Grammar Induction](https://md.research.elamur.ai/free/en/papers/kim-2019-compound-probabilistic-context-free-grammars/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yoon Kim, Chris Dyer, Alexander Rush*2019 · 201 citations*
545. [**IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages arXiv:2305.16307**Jay Gala, Pranjal A. Chitale, Raghavan AK, et al.*2023 · 200 citations*](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/)
546. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
547. **[Block Pruning For Faster Transformers](https://md.research.elamur.ai/free/en/papers/lagunas-2021-block-pruning-faster-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**François Lagunas, Ella Charlaix, Victor Sanh, et al.*2021 · 199 citations*
548. **[Position Information in Transformers: An Overview](https://md.research.elamur.ai/free/en/papers/dufter-2022-position-information-transformers-overview/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Philipp Dufter, Martin Schmitt, Hinrich Schütze*2022 · 198 citations*
549. [**COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining arXiv:2102.08473**Yu Meng, Chenyan Xiong, Payal Bajaj, et al.*2021 · 198 citations*](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)
550. [**A Convolutional Attention Network for Extreme Summarization of Source Code arXiv:1602.03001**Miltiadis Allamanis, Hao Peng, Charles Sutton*2016 · 198 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2016-convolutional-attention-network-extreme-summarization/)
551. **[Design Challenges and Misconceptions in Neural Sequence Labeling](https://md.research.elamur.ai/free/en/papers/yang-2018-design-challenges-misconceptions-neural-sequence/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Jie Yang, Shuailong Liang, Yue Zhang*2018 · 197 citations*
552. **[Efficient and Robust Question Answering from Minimal Context over Documents](https://md.research.elamur.ai/free/en/papers/min-2018-efficient-robust-question-answering-minimal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sewon Min, Victor Zhong, Richard Socher, et al.*2018 · 197 citations*
553. **[The Right Tool for the Job: Matching Model and Instance Complexities](https://md.research.elamur.ai/free/en/papers/schwartz-2020-right-tool-job-matching-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, et al.*2020 · 196 citations*
554. **[Representation of Linguistic Form and Function in Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/kadar-2017-representation-linguistic-form-function-recurrent/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Ákos Kádár, Grzegorz Chrupała, Afra Alishahi*2017 · 192 citations*
555. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations*
556. **[Towards Universal Dialogue State Tracking](https://md.research.elamur.ai/free/en/papers/ren-2018-universal-dialogue-state-tracking/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liliang Ren, Kaige Xie, Lu Chen, et al.*2018 · 191 citations*
557. **[Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles](https://md.research.elamur.ai/free/en/papers/cross-2016-span-based-constituency-parsing-structure/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**James Cross, Liang Huang*2016 · 191 citations*
558. **[A Dataset for Multi-Target Stance Detection](https://md.research.elamur.ai/free/en/papers/sobhani-2017-dataset-multi-target-stance-detection/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Parinaz Sobhani, Diana Inkpen, Xiaodan Zhu*2017 · 190 citations*
559. [**Grounded Language Learning in a Simulated 3D World arXiv:1706.06551**Karl Moritz Hermann, Felix Hill, Simon Green, et al.*2017 · 190 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2017-grounded-language-learning-simulated-3d/)
560. [**Transformers without Tears: Improving the Normalization of Self-Attention arXiv:1910.05895**Toan Q. Nguyen, Julian Salazar*2019 · 189 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2019-transformers-tears-improving-normalization-self/)
561. **[AudioCaps: Generating Captions for Audios in The Wild](https://md.research.elamur.ai/free/en/papers/kim-2019-audiocaps-generating-captions-audios-wild/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, et al.*2019 · 187 citations*
562. **[An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking](https://md.research.elamur.ai/free/en/papers/xu-2018-end-end-approach-handling-unknown/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Puyang Xu, Qi Hu*2018 · 187 citations*
563. **[Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://md.research.elamur.ai/free/en/papers/liu-2023-deja-vu-contextual-sparsity-efficient/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Zichang Liu, Jue Wang, Tri Dao, et al.*2023 · 185 citations*
564. **[Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/thompson-2019-overcoming-catastrophic-forgetting-during-domain/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Brian Thompson, Jeremy Gwinnup, Huda Khayrallah, et al.*2019 · 185 citations*
565. **[Re2G: Retrieve, Rerank, Generate](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, et al.*2022 · 184 citations*
566. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
567. [**The False Promise of Imitating Proprietary LLMs arXiv:2305.15717**Arnav Gudibande, Eric Wallace, Charlie Snell, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/gudibande-2023-false-promise-imitating-proprietary-llms/)
568. **[Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/pan-2020-modeling-intra-inter-modality-incongruity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongliang Pan, Zheng Lin, Peng Fu, et al.*2020 · 182 citations*
569. **[MultiCoNER: A Large-scale Multilingual Dataset for Complex Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/malmasi-2022-multiconer-large-scale-multilingual-dataset/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Shervin Malmasi, Anjie Fang, Besnik Fetahu, et al.*2022 · 181 citations*
570. [**8-bit Optimizers via Block-wise Quantization arXiv:2110.02861**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 181 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
571. [**Representation Degeneration Problem in Training Natural Language Generation Models arXiv:1907.12009**Jun Gao, Di He, Xu Tan, et al.*2019 · 181 citations*](https://md.research.elamur.ai/free/en/papers/gao-2019-representation-degeneration-problem-training-natural/)
572. [**Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads arXiv:1901.05758**Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/)
573. **[Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control](https://md.research.elamur.ai/free/en/papers/yu-2019-rethinking-cooperative-rationalization-introspective-extraction/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mo Yu, Shiyu Chang, Yang Zhang, et al.*2019 · 179 citations*
574. [**Improving Neural Language Models with a Continuous Cache arXiv:1612.04426**Edouard Grave, Armand Joulin, Nicolas Usunier*2016 · 179 citations*](https://md.research.elamur.ai/free/en/papers/grave-2016-improving-neural-language-models-continuous/)
575. **[Non-Autoregressive Machine Translation with Latent Alignments](https://md.research.elamur.ai/free/en/papers/saharia-2020-non-autoregressive-machine-translation-latent/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2020 · 178 citations*
576. **[Integrating Transformer and Paraphrase Rules for Sentence Simplification](https://md.research.elamur.ai/free/en/papers/zhao-2018-integrating-transformer-paraphrase-rules-sentence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sanqiang Zhao, Rui Meng, Daqing He, et al.*2018 · 178 citations*
577. [**Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets arXiv:2201.02177**Alethea Power, Yuri Burda, Harri Edwards, et al.*2022 · 177 citations*](https://md.research.elamur.ai/free/en/papers/power-2022-grokking-generalization-overfitting-small-algorithmic/)
578. [**BASE Layers: Simplifying Training of Large, Sparse Models arXiv:2103.16716**Mike Lewis, Shruti Bhosale, Tim Dettmers, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2021-base-layers-simplifying-training-large/)
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580. [**Adaptive Input Representations for Neural Language Modeling arXiv:1809.10853**Alexei Baevski, Michael Auli*2018 · 177 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2018-adaptive-input-representations-neural-language/)
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586. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
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590. **[Soft Contextual Data Augmentation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gao-2019-soft-contextual-data-augmentation-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fei Gao, Jinhua Zhu, Lijun Wu, et al.*2019 · 172 citations*
591. **[Data Augmentation via Dependency Tree Morphing for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/sahin-2018-data-augmentation-dependency-tree-morphing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gözde Gül Şahin, Mark Steedman*2018 · 171 citations*
592. [**Capabilities of Gemini Models in Medicine arXiv:2404.18416**Khaled Saab, Tao Tu, Wei-Hung Weng, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/saab-2024-capabilities-gemini-models-medicine/)
593. [**Tutel: Adaptive Mixture-of-Experts at Scale MLSys 2023**Changho Hwang, Wei Cui, Yifan Xiong, et al.*2022 · 168 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2022-tutel-adaptive-mixture-experts-scale/)
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595. **[Few-Shot Text Generation with Natural Language Instructions](https://md.research.elamur.ai/free/en/papers/schick-2021-few-shot-text-generation-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schütze*2021 · 167 citations*
596. **[Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation](https://md.research.elamur.ai/free/en/papers/kumar-2019-submodular-optimization-based-diverse-paraphrasing/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, et al.*2019 · 167 citations*
597. **[UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning](https://md.research.elamur.ai/free/en/papers/mao-2022-unipelt-unified-framework-parameter-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuning Mao, Lambert Mathias, Rui Hou, et al.*2022 · 165 citations*
598. **[Stronger Baselines for Trustable Results in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/denkowski-2017-stronger-baselines-trustable-results-neural/) [Workshop on Neural Machine Translation](https://md.research.elamur.ai/free/en/venues/workshop-on-neural-machine-translation/)**Michael Denkowski, Graham Neubig*2017 · 165 citations*
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600. [**Neural Language Correction with Character-Based Attention arXiv:1603.09727**Ziang Xie, Anand Avati, Naveen Arivazhagan, et al.*2016 · 164 citations*](https://md.research.elamur.ai/free/en/papers/xie-2016-neural-language-correction-character-based/)

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