---
title: "Who cites Kevin P. Murphy — 1,175 papers of our library"
description: "1,175 papers of the Elamur Research library cite the works of Kevin P. Murphy, found in their bibliographies · page 1 of 2 · 1–100; the most…"
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---
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# Who cites Kevin P. Murphy

1,264 citations of their works from 1,175 papers of our library (of the 149,439 whose bibliographies we read) · 139 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Kevin P. Murphy](https://md.research.elamur.ai/free/en/authors/kevin-p-murphy/)

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1. [**An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale arXiv:2010.11929**Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, et al.*2020 · 23,726 citations*](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/)
2. **[Momentum Contrast for Unsupervised Visual Representation Learning](https://md.research.elamur.ai/free/en/papers/he-2019-momentum-contrast-unsupervised-visual-representation/) [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/)**Kaiming He, Haoqi Fan, Yuxin Wu, et al.*2019 · 13,302 citations*
3. [**YOLOv4: Optimal Speed and Accuracy of Object Detection arXiv:2004.10934**Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao*2020 · 10,403 citations*](https://md.research.elamur.ai/free/en/papers/bochkovskiy-2020-yolov4-optimal-speed-accuracy-object/)
4. [**MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications arXiv:1704.04861**Andrew G. Howard, Menglong Zhu, Bo Chen, et al.*2017 · 9,729 citations*](https://md.research.elamur.ai/free/en/papers/howard-2017-mobilenets-efficient-convolutional-neural-networks/)
5. **[Learning Transferable Architectures for Scalable Image Recognition](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/) [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/)**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,583 citations*
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. **[EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Mingxing Tan, Quoc V. Le*2019 · 5,416 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. **[CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,967 citations*
10. **[Improved Baselines with Visual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/) [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/)**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,614 citations*
11. **[Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://md.research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/) [IEEE International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-international-conference-on-computer-vision-iccv/)**Chen Sun, Abhinav Shrivastava, Saurabh Singh, et al.*2017 · 2,512 citations*
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. **[Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Piyush Sharma, Nan Ding, Sebastian Goodman, et al.*2018 · 2,348 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. **[Designing Network Design Spaces](https://md.research.elamur.ai/free/en/papers/radosavovic-2020-designing-network-design-spaces/) [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/)**Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, et al.*2020 · 2,040 citations*
17. [**Focal Loss for Dense Object Detection arXiv:1708.02002**Tsung-Yi Lin, Priya Goyal, Ross Girshick, et al.*2017 · 1,995 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-focal-loss-dense-object-detection/)
18. **[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*
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. [**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/)
21. **[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*
22. **[FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://md.research.elamur.ai/free/en/papers/wu-2018-fbnet-hardware-aware-efficient-convnet/) [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/)**Bichen Wu, Xiaoliang Dai, Peizhao Zhang, et al.*2018 · 1,405 citations*
23. [**Generative Modeling by Estimating Gradients of the Data Distribution arXiv:1907.05600**Yang Song, Stefano Ermon*2019 · 1,274 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-generative-modeling-estimating-gradients-data/)
24. **[Modeling Context in Referring Expressions](https://md.research.elamur.ai/free/en/papers/yu-2016-modeling-context-referring-expressions/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Licheng Yu, Patrick Poirson, Shan Yang, et al.*2016 · 1,203 citations*
25. [**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/)
26. **[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*
27. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
28. [**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/)
29. [**Do As I Can, Not As I Say: Grounding Language in Robotic Affordances arXiv:2204.01691**Michael Ahn, Anthony Brohan, Noah Brown, et al.*2022 · 941 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2022-i-i-say-grounding-language/)
30. [**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/)
31. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 780 citations*
32. **[Unified Pre-training for Program Understanding and Generation](https://md.research.elamur.ai/free/en/papers/ahmad-2021-unified-pre-training-program-understanding/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, et al.*2021 · 750 citations*
33. **[Integrating Multimodal Information in Large Pretrained Transformers](https://md.research.elamur.ai/free/en/papers/rahman-2020-integrating-multimodal-information-large-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wasifur Rahman, Md Kamrul Hasan, Sangwu Lee, et al.*2020 · 746 citations*
34. **[Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://md.research.elamur.ai/free/en/papers/chen-2019-progressive-differentiable-architecture-search-bridging/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Xin Chen, Lingxi Xie, Jun Wu, et al.*2019 · 732 citations*
35. [**Efficient Neural Architecture Search via Parameter Sharing arXiv:1802.03268**Hieu Pham, Melody Y. Guan, Barret Zoph, et al.*2018 · 699 citations*](https://md.research.elamur.ai/free/en/papers/pham-2018-efficient-neural-architecture-search-parameter/)
36. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 670 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
37. **[LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Xu, Yiheng Xu, Tengchao Lv, et al.*2021 · 627 citations*
38. **[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*
39. **[How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhe Chen, Weiyun Wang, Hao Tian, et al.*2024 · 541 citations*
40. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
41. [**Large Language Models Can Be Easily Distracted by Irrelevant Context arXiv:2302.00093**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 534 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
42. [**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/)
43. **[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*
44. [**ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware arXiv:1812.00332**Han Cai, Ligeng Zhu, Song Han*2018 · 479 citations*](https://md.research.elamur.ai/free/en/papers/cai-2018-proxylessnas-direct-neural-architecture-search/)
45. [**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/)
46. **[Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 439 citations*
47. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks arXiv:2312.14238**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 435 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
48. **[Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://md.research.elamur.ai/free/en/papers/khot-2022-decomposed-prompting-modular-approach/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Tushar Khot, Harsh Trivedi, Matthew Finlayson, et al.*2023 · 390 citations*
49. [**Green AI arXiv:1907.10597**Roy Schwartz, Jesse Dodge, Noah A. Smith, et al.*2019 · 389 citations*](https://md.research.elamur.ai/free/en/papers/schwartz-2019-green-ai/)
50. **[CogVLM: Visual Expert for Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Weihan Wang, Qingsong Lv, Wenmeng Yu, et al.*2023 · 383 citations*
51. **[VILA: On Pre-training for Visual Language Models](https://md.research.elamur.ai/free/en/papers/lin-2023-vila-pre-training-visual-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/)**Ji Lin, Hongxu Yin, Wei Ping, et al.*2023 · 381 citations*
52. [**Qwen3-VL Technical Report arXiv:2511.21631**Shuai Bai, Yuxuan Cai, Ruizhe Chen, et al.*2025 · 370 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/)
53. **[VideoChat: Chat-Centric Video Understanding](https://md.research.elamur.ai/free/en/papers/li-2023-videochat-chat-centric-video-understanding/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**KunChang Li, Yinan He, Yi Wang, et al.*2023 · 365 citations*
54. [**Improving alignment of dialogue agents via targeted human judgements arXiv:2209.14375**Amelia Glaese, Nat McAleese, Maja Trębacz, et al.*2022 · 362 citations*](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/)
55. [**Solving math word problems with process- and outcome-based feedback arXiv:2211.14275**Jonathan Uesato, Nate Kushman, Ramana Kumar, et al.*2022 · 355 citations*](https://md.research.elamur.ai/free/en/papers/uesato-2022-solving-math-word-problems-process/)
56. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
57. [**ALFWorld: Aligning Text and Embodied Environments for Interactive Learning arXiv:2010.03768**Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, et al.*2020 · 322 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2020-alfworld-aligning-text-embodied-environments/)
58. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
59. [**Kosmos-2: Grounding Multimodal Large Language Models to the World arXiv:2306.14824**Zhiliang Peng, Wenhui Wang, Li Dong, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-kosmos-2-grounding-multimodal-large/)
60. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines arXiv:2310.03714**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
61. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/)
62. **[LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-llama-vid-image-worth-2/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yanwei Li, Chengyao Wang, Jiaya Jia*2023 · 262 citations*
63. **[Towards Debiasing Sentence Representations](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 262 citations*
64. **[SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!](https://md.research.elamur.ai/free/en/papers/sharma-2020-semeval-2020-task-8-memotion/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Chhavi Sharma, Deepesh Bhageria, William Scott, et al.*2020 · 261 citations*
65. **[Efficient, Feature-based, Conditional Random Field Parsing](https://md.research.elamur.ai/free/en/papers/finkel-2008-efficient-feature-based-conditional-random/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jenny Rose Finkel, Alex Kleeman, Christopher D. Manning*2008 · 261 citations*
66. [**Once-for-All: Train One Network and Specialize it for Efficient Deployment arXiv:1908.09791**Han Cai, Chuang Gan, Tianzhe Wang, et al.*2019 · 252 citations*](https://md.research.elamur.ai/free/en/papers/cai-2019-once-all-train-one-network/)
67. **[Language Models can Solve Computer Tasks](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 249 citations*
68. **[Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xueliang Zhao, Wei Wu, Can Xu, et al.*2020 · 246 citations*
69. [**Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought arXiv:2210.01240**Abulhair Saparov, He He*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/)
70. [**Qwen2.5-Omni Technical Report arXiv:2503.20215**Jin Xu, Zhifang Guo, Jinzheng He, et al.*2025 · 227 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen2-5-omni-technical/)
71. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP arXiv:2212.14024**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 222 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
72. [**FOLIO: Natural Language Reasoning with First-Order Logic arXiv:2209.00840**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
73. [**InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models arXiv:2504.10479**Jinguo Zhu, Weiyun Wang, Zhe Chen, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)
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