---
title: "Who cites Piyush Sharma — 2,380 papers of our library"
description: "2,380 papers of the Elamur Research library cite the works of Piyush Sharma, found in their bibliographies · page 1 of 4 · 1–100; the most cited first."
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---
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# Who cites Piyush Sharma

2,405 citations of their works from 2,380 papers of our library (of the 149,439 whose bibliographies we read) · 378 with a page here, the most cited first · page 1 of 4 · 1–100 · [← Piyush Sharma](https://md.research.elamur.ai/free/en/authors/piyush-sharma/)

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1. **[Transformers: State-of-the-Art Natural Language Processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thomas Wolf, Lysandre Debut, Victor Sanh, et al.*2020 · 15,755 citations*
2. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
3. **[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,856 citations*
4. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
5. [**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/)
6. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
7. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding arXiv:1906.08237**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,857 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
8. [**Measuring Massive Multitask Language Understanding arXiv:2009.03300**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,620 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
9. [**Scaling Laws for Neural Language Models arXiv:2001.08361**Jared Kaplan, Sam McCandlish, Tom Henighan, et al.*2020 · 3,434 citations*](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/)
10. [**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/)
11. [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 3,028 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/)
12. **[UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,418 citations*
13. **[TinyBERT: Distilling BERT for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoqi Jiao, Yichun Yin, Lifeng Shang, et al.*2020 · 2,378 citations*
14. [**ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks arXiv:1908.02265**Jiasen Lu, Dhruv Batra, Devi Parikh, et al.*2019 · 2,302 citations*](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/)
15. [**Flamingo: a Visual Language Model for Few-Shot Learning arXiv:2204.14198**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,246 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
16. [**BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597**Junnan Li, Dongxu Li, Silvio Savarese, et al.*2023 · 2,219 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-blip-2-bootstrapping-language-image/)
17. **[Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Xiujun Li, Xi Yin, Chunyuan Li, et al.*2020 · 1,877 citations*
18. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
19. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
20. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
21. [**BERTopic: Neural topic modeling with a class-based TF-IDF procedure arXiv:2203.05794**Maarten Grootendorst*2022 · 1,737 citations*](https://md.research.elamur.ai/free/en/papers/grootendorst-2022-bertopic-neural-topic-modeling-class/)
22. **[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,615 citations*
23. [**VisualBERT: A Simple and Performant Baseline for Vision and Language arXiv:1908.03557**Liunian Harold Li, Mark Yatskar, Da Yin, et al.*2019 · 1,568 citations*](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/)
24. **[Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,556 citations*
25. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,551 citations*
26. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
27. [**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/)
28. [**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/)
29. **[LEGAL-BERT: The Muppets straight out of Law School](https://md.research.elamur.ai/free/en/papers/chalkidis-2020-legal-bert-muppets-straight-out/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, et al.*2020 · 1,493 citations*
30. **[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*
31. [**LAION-5B: An open large-scale dataset for training next generation image-text models arXiv:2210.08402**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
32. **[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,455 citations*
33. [**LaMDA: Language Models for Dialog Applications arXiv:2201.08239**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,344 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
34. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
35. [**AraBERT: Transformer-based Model for Arabic Language Understanding arXiv:2003.00104**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,304 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
36. **[CamemBERT: a Tasty French Language Model](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.*2019 · 1,269 citations*
37. [**MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models arXiv:2304.10592**Deyao Zhu, Jun Chen, Xiaoqian Shen, et al.*2023 · 1,141 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/)
38. **[It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Timo Schick, Hinrich Schütze*2021 · 1,132 citations*
39. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,066 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
40. **[Vision-Language Models for Vision Tasks: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-vision-language-models-vision-tasks/) [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-pattern-analysis-and-machine-intelligence/)**Jingyi Zhang, Jiaxing Huang, Sheng Jin, et al.*2023 · 1,047 citations*
41. **[Evaluating Object Hallucination in Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yifan Li, Yifan Du, Kun Zhou, et al.*2023 · 1,004 citations*
42. **[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*
43. [**Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond arXiv:2308.12966**Jinze Bai, Shuai Bai, Shusheng Yang, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
44. **[CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://md.research.elamur.ai/free/en/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.*2020 · 897 citations*
45. **[UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 857 citations*
46. [**Unsupervised Dense Information Retrieval with Contrastive Learning arXiv:2112.09118**Gautier Izacard, Mathilde Caron, Lucas Hosseini, et al.*2021 · 836 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/)
47. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
48. [**ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision arXiv:2102.03334**Wonjae Kim, Bokyung Son, Ildoo Kim*2021 · 804 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-vilt-vision-language-transformer-convolution/)
49. [**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/)
50. **[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*
51. **[IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 732 citations*
52. **[CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 666 citations*
53. **[A Frustratingly Easy Approach for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zexuan Zhong, Danqi Chen*2020 · 631 citations*
54. **[Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://md.research.elamur.ai/free/en/papers/lin-2023-video-llava-learning-united-visual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bin Lin, Yang Ye, Bin Zhu, et al.*2023 · 628 citations*
55. **[A Robustly Optimized BERT Pre-training Approach with Post-training](https://md.research.elamur.ai/free/en/papers/zhuang-2021-robustly-optimized-bert-pre-training/) [Chinese National Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/ccl/)**Liu Zhuang, Lin Wayne, Shi Ya, et al.*2021 · 587 citations*
56. [**What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams arXiv:2009.13081**Di Jin, Eileen Pan, Nassim Oufattole, et al.*2020 · 567 citations*](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/)
57. **[Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer*2021 · 542 citations*
58. **[DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 516 citations*
59. **[A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 509 citations*
60. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision arXiv:2108.10904**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 481 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
61. **[HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Linjie Li, Yen-Chun Chen, Yu Cheng, et al.*2020 · 481 citations*
62. [**ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic arXiv:2101.01785 · ACL-2021 camera ready version**Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi*2021 · 478 citations*](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/)
63. [**Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection arXiv:2303.05499**Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al.*2023 · 472 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-grounding-dino-marrying-dino-grounded/)
64. [**Large Language Models: A Survey arXiv:2402.06196**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 471 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
65. [**CogView: Mastering Text-to-Image Generation via Transformers arXiv:2105.13290**Ming Ding, Zhuoyi Yang, Wenyi Hong, et al.*2021 · 469 citations*](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)
66. [**MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts arXiv:2310.02255**Pan Lu, Hritik Bansal, Tony Xia, et al.*2023 · 468 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/)
67. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework arXiv:2202.03052**Peng Wang, An Yang, Rui Men, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
68. **[MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/) [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/)**Kunchang Li, Yali Wang, Yinan He, et al.*2023 · 463 citations*
69. **[ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://md.research.elamur.ai/free/en/papers/chen-2023-sharegpt4v-improving-large-multi-modal/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2023 · 454 citations*
70. **[LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 452 citations*
71. **[Pretrained Transformers Improve Out-of-Distribution Robustness](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 452 citations*
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