---
title: "Who cites Benoit Steiner — 4,980 papers of our library"
description: "4,980 papers of the Elamur Research library cite the works of Benoit Steiner, found in their bibliographies · page 1 of 5 · 1–100; the most cited first."
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---
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# Who cites Benoit Steiner

5,349 citations of their works from 4,980 papers of our library (of the 149,439 whose bibliographies we read) · 473 with a page here, the most cited first · page 1 of 5 · 1–100 · [← Benoit Steiner](https://md.research.elamur.ai/free/en/authors/benoit-steiner/)

**1** [2](https://md.research.elamur.ai/free/en/authors/benoit-steiner/cited-by/page/2/) … [5](https://md.research.elamur.ai/free/en/authors/benoit-steiner/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/authors/benoit-steiner/cited-by/page/2/)

1. **[Array Programming with NumPy](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,607 citations*
2. [**TensorFlow: A system for large-scale machine learning arXiv:1605.08695**Martin Abadi, Paul Barham, Jianmin Chen, et al.*2016 · 12,711 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-system-large-scale-machine/)
3. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
4. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
5. [**Inductive Representation Learning on Large Graphs arXiv:1706.02216**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,795 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
6. **[Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,832 citations*
7. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
8. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
9. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
10. [**What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? arXiv:1703.04977**Alex Kendall, Yarin Gal*2017 · 2,870 citations*](https://md.research.elamur.ai/free/en/papers/kendall-2017-uncertainties-need-bayesian-deep-learning/)
11. [**Robust Speech Recognition via Large-Scale Weak Supervision arXiv:2212.04356**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,546 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/)
12. **[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,861 citations*
13. [**AllenNLP: A Deep Semantic Natural Language Processing Platform arXiv:1803.07640**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
14. [**Universal Sentence Encoder arXiv:1803.11175**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
15. **[Universal Sentence Encoder for English](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,546 citations*
16. [**Zero-Shot Text-to-Image Generation arXiv:2102.12092**Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/)
17. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation arXiv:2201.12086**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,523 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
18. [**Understanding Black-box Predictions via Influence Functions arXiv:1703.04730**Pang Wei Koh, Percy Liang*2017 · 1,514 citations*](https://md.research.elamur.ai/free/en/papers/koh-2017-understanding-black-box-predictions-influence/)
19. **[Pre-Training with Whole Word Masking for Chinese BERT](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/) [IEEE/ACM Transactions on Audio Speech and Language Processing](https://md.research.elamur.ai/free/en/venues/ieee-acm-transactions-on-audio-speech-and-language-processing/)**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,490 citations*
20. **[Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/) [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/)**Peter Anderson, Qi Wu, Damien Teney, et al.*2017 · 1,414 citations*
21. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning arXiv:1802.04799**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,384 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/)
22. **[RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,306 citations*
23. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
24. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
25. [**DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset arXiv:1710.03957**Yanran Li, Hui Su, Xiaoyu Shen, et al.*2017 · 1,206 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-dailydialog-manually-labelled-multi-turn/)
26. [**OpenAI o1 System Card arXiv:2412.16720**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
27. **[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaowei Zheng, Richong Zhang, Junhao Zhang, et al.*2024 · 1,048 citations*
28. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
29. [**QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding arXiv:1610.02132**Dan Alistarh, Demjan Grubic, Jerry Li, et al.*2016 · 962 citations*](https://md.research.elamur.ai/free/en/papers/alistarh-2016-qsgd-communication-efficient-sgd-gradient/)
30. **[PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Jason Ansel, Edward Yang, Horace He, et al.*2024 · 946 citations*
31. **[4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://md.research.elamur.ai/free/en/papers/wu-2023-4d-gaussian-splatting-real-time/) [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/)**Guanjun Wu, Taoran Yi, Jiemin Fang, et al.*2023 · 887 citations*
32. [**Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution arXiv:2409.12191**Peng Wang, Shuai Bai, Sinan Tan, et al.*2024 · 860 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/)
33. **[ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.*2020 · 832 citations*
34. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
35. [**Revisiting Pre-Trained Models for Chinese Natural Language Processing arXiv:2004.13922**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2020 · 790 citations*](https://md.research.elamur.ai/free/en/papers/cui-2020-revisiting-pre-trained-models-chinese/)
36. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) [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/)**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 769 citations*
37. **[SGLang: Efficient Execution of Structured Language Model Programs](https://md.research.elamur.ai/free/en/papers/zheng-2023-sglang-efficient-execution-structured-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, et al.*2023 · 729 citations*
38. [**Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations MAPL 2019**Philippe Tillet, H. T. Kung, David Cox*2019 · 729 citations*](https://md.research.elamur.ai/free/en/papers/tillet-2019-triton-intermediate-language-compiler-tiled/)
39. [**Horovod: fast and easy distributed deep learning in TensorFlow arXiv:1802.05799**Alexander Sergeev, Mike Del Balso*2018 · 695 citations*](https://md.research.elamur.ai/free/en/papers/sergeev-2018-horovod-fast-easy-distributed-deep/)
40. [**HybridFlow: A Flexible and Efficient RLHF Framework arXiv:2409.19256**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 624 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/)
41. [**SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis arXiv:2307.01952**Dustin Podell, Zion English, Kyle Lacey, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/podell-2023-sdxl-improving-latent-diffusion-models/)
42. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension arXiv:1804.09541**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 597 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
43. **[Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 586 citations*
44. [**gpt-oss-120b & gpt-oss-20b Model Card arXiv:2508.10925**OpenAI: Sandhini Agarwal, Lama Ahmad, Jason Ai, et al.*2025 · 576 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2025-gpt-oss-120b-gpt-oss/)
45. **[Massive Exploration of Neural Machine Translation Architectures](https://md.research.elamur.ai/free/en/papers/britz-2017-massive-exploration-neural-machine-translation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Denny Britz, Anna Goldie, Minh-Thang Luong, et al.*2017 · 574 citations*
46. **[Multilingual Universal Sentence Encoder for Semantic Retrieval](https://md.research.elamur.ai/free/en/papers/yang-2020-multilingual-universal-sentence-encoder-semantic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yinfei Yang, Daniel Cer, Amin Ahmad, et al.*2020 · 570 citations*
47. [**HDLTex: Hierarchical Deep Learning for Text Classification ICMLA 2017**Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, et al.*2017 · 532 citations*](https://md.research.elamur.ai/free/en/papers/kowsari-2017-hdltex-hierarchical-deep-learning-text/)
48. [**Training data-efficient image transformers & distillation through attention arXiv:2012.12877**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 524 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/)
49. **[SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-sparse-lexical-expansion/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant*2021 · 512 citations*
50. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
51. [**Regularizing Neural Networks by Penalizing Confident Output Distributions arXiv:1701.06548**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 496 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
52. [**Texygen: A Benchmarking Platform for Text Generation Models arXiv:1802.01886**Yaoming Zhu, Sidi Lu, Lei Zheng, et al.*2018 · 495 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2018-texygen-benchmarking-platform-text-generation/)
53. [**DyNet: The Dynamic Neural Network Toolkit arXiv:1701.03980**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 489 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
54. [**Orca: A Distributed Serving System for Transformer-Based Generative Models OSDI 2022**Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, et al.*2022 · 478 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-orca-distributed-serving-system-transformer/)
55. **[Are All Languages Created Equal in Multilingual BERT?](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Shijie Wu, Mark Dredze*2020 · 460 citations*
56. [**In-Datacenter Performance Analysis of a Tensor Processing Unit arXiv:1704.04760**Norman P. Jouppi; Cliff Young; Nishant Patil; David Patterson; Gaurav Agrawal; Raminder Bajwa; Sarah Bates; Suresh Bhatia; Nan Boden; Al Borchers; Rick Boyle; Pierre-luc Cantin; Clifford Chao; Chris Clark; Jeremy Coriell; Mike Daley; Matt Dau; Jeffrey Dean; Ben Gelb; Tara Vazir Ghaemmaghami; Rajendra Gottipati; William Gulland; Robert Hagmann; C. Richard Ho; Doug Hogberg; John Hu; Robert Hundt; Dan Hurt; Julian Ibarz; Aaron Jaffey; Alek Jaworski; Alexander Kaplan; Harshit Khaitan; Andy Koch; Naveen Kumar; Steve Lacy; James Laudon; James Law; Diemthu Le; Chris Leary; Zhuyuan Liu; Kyle Lucke; Alan Lundin; Gordon MacKean; Adriana Maggiore; Maire Mahony; Kieran Miller; Rahul Nagarajan; Ravi Narayanaswami; Ray Ni; Kathy Nix; Thomas Norrie; Mark Omernick; Narayana Penukonda; Andy Phelps; Jonathan Ross; Matt Ross; Amir Salek; Emad Samadiani; Chris Severn; Gregory Sizikov; Matthew Snelham; Jed Souter; Dan Steinberg; Andy Swing; Mercedes Tan; Gregory Thorson; Bo Tian; Horia Toma; Erick Tuttle; Vijay Vasudevan; Richard Walter; Walter Wang; Eric Wilcox;, Doe Hyun Yoon*2017 · 449 citations*](https://md.research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/)
57. [**Adversarial Training Methods for Semi-Supervised Text Classification arXiv:1605.07725**Takeru Miyato, Andrew M. Dai, Ian Goodfellow*2016 · 433 citations*](https://md.research.elamur.ai/free/en/papers/miyato-2016-adversarial-training-methods-semi-supervised/)
58. [**Hindsight Experience Replay arXiv:1707.01495**Marcin Andrychowicz, Filip Wolski, Alex Ray, et al.*2017 · 421 citations*](https://md.research.elamur.ai/free/en/papers/andrychowicz-2017-hindsight-experience-replay/)
59. [**The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes arXiv:2005.04790**Douwe Kiela, Hamed Firooz, Aravind Mohan, et al.*2020 · 420 citations*](https://md.research.elamur.ai/free/en/papers/kiela-2020-hateful-memes-challenge-detecting-hate/)
60. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
61. **[Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning](https://md.research.elamur.ai/free/en/papers/gordon-2020-compressing-bert-studying-effects-weight/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Mitchell Gordon, Kevin Duh, Nicholas Andrews*2020 · 407 citations*
62. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU arXiv:2303.06865**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 402 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/)
63. [**Editing Models with Task Arithmetic arXiv:2212.04089**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
64. **[ViperGPT: Visual Inference via Python Execution for Reasoning](https://md.research.elamur.ai/free/en/papers/suris-2023-vipergpt-visual-inference-python-execution/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Didac Suris, Sachit Menon, Carl Vondrick*2023 · 383 citations*
65. **[Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.*2024 · 382 citations*
66. **[emoji2vec: Learning Emoji Representations from their Description](https://md.research.elamur.ai/free/en/papers/eisner-2016-emoji2vec-learning-emoji-representations-their/) [International Workshop on Natural Language Processing for Social Media](https://md.research.elamur.ai/free/en/venues/socialnlp/)**Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, et al.*2016 · 360 citations*
67. [**RobBERT: a Dutch RoBERTa-based Language Model arXiv:2001.06286**Pieter Delobelle, Thomas Winters, Bettina Berendt*2020 · 349 citations*](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/)
68. [**PyTorch Distributed: Experiences on Accelerating Data Parallel Training arXiv:2006.15704**Shen Li, Yanli Zhao, Rohan Varma, et al.*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-pytorch-distributed-experiences-accelerating-data/)
69. [**Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes arXiv:1807.11205**Xianyan Jia, Shutao Song, Wei He, et al.*2018 · 348 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-highly-scalable-deep-learning-training/)
70. **[Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Luyang Huang, Shuyang Cao, Nikolaus Parulian, et al.*2021 · 336 citations*
71. **[Few-NERD: A Few-shot Named Entity Recognition Dataset](https://md.research.elamur.ai/free/en/papers/ding-2021-few-nerd-few-shot-named/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ning Ding, Guangwei Xu, Yulin Chen, et al.*2021 · 336 citations*
72. **[Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://md.research.elamur.ai/free/en/papers/gao-2022-unsupervised-corpus-aware-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luyu Gao, Jamie Callan*2022 · 328 citations*
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