🔗 Who cites «DataComp: In search of the next generation of multimodal datasets»

119 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · ← back to «DataComp: In search of the next generation of multimodal datasets»

1. Qwen2.5-VL Technical ReportarXiv:2502.13923Shuai Bai, Keqin Chen, Xuejing Liu, et al.2025 · 1,031 citations
2. Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and BeyondarXiv:2308.12966Jinze Bai, Shuai Bai, Shusheng Yang, et al.2023 · 970 citations
3. ShareGPT4V: Improving Large Multi-Modal Models with Better CaptionsLecture notes in computer scienceLin Chen, Jinsong Li, Xiaoyi Dong, et al.2023 · 454 citations
4. Kimi k1.5: Scaling Reinforcement Learning with LLMsarXiv:2501.12599Kimi Team, Angang Du, Bofei Gao, et al.2025 · 431 citations
5. MM-LLMs: Recent Advances in MultiModal Large Language ModelsFindings of the Association for Computational LinguisticsDuzhen Zhang, Yahan Yu, Jiahua Dong, et al.2024 · 320 citations
6. Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMsConference on Neural Information Processing Systems (NeurIPS)Shengbang Tong, Ellis Brown, Penghao Wu, et al.2024 · 208 citations
7. Large Language Model as Attributed Training Data Generator: A Tale of Diversity and BiasConference on Neural Information Processing Systems (NeurIPS)Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.2023 · 202 citations
8. OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text DocumentsConference on Neural Information Processing Systems (NeurIPS)Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.2023 · 197 citations
9. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.2024 · 191 citations
10. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
11. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Pratyush Maini, Skyler Seto, Richard Bai, et al.2024 · 90 citations
12. GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement LearningarXiv:2507.01006GLM-V Team, Wenyi Hong, Wenmeng Yu, et al.2025 · 89 citations
13. Kimi-VL Technical ReportarXiv:2504.07491Kimi Team, Angang Du, Bohong Yin, et al.2025 · 70 citations
14. Kimi K2.5: Visual Agentic IntelligencearXiv:2602.02276Kimi Team, Tongtong Bai, Yifan Bai, et al.2026 · 66 citations
15. mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language ModelsarXiv:2408.04840Jiabo Ye, Haiyang Xu, Haowei Liu, et al.2024 · 60 citations
16. Teaching Arithmetic to Small TransformersarXiv:2307.03381Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.2023 · 57 citations
17. CogVLM2: Visual Language Models for Image and Video UnderstandingarXiv:2408.16500Wenyi Hong, Weihan Wang, Ming Ding, et al.2024 · 52 citations
18. Language models scale reliably with over-training and on downstream tasksarXiv:2403.08540Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.2024 · 46 citations
19. NVLM: Open Frontier-Class Multimodal LLMsarXiv:2409.11402Wenliang Dai, Nayeon Lee, Boxin Wang, et al.2024 · 44 citations
20. MegaPairs: Massive Data Synthesis for Universal Multimodal RetrievalAnnual Meeting of the Association for Computational Linguistics (ACL)Junjie Zhou, Yongping Xiong, Zheng Liu, et al.2025 · 26 citations
21. Reformulate and Create, Don't Translate: Creating Natural Prompts for Underserved LanguagesLanguage Resources and Evaluation Conference (LREC)Annika Simonsen, Mathias Stenlund, Lars Bungum, et al.2026 · 1 citation
22. Revisiting Audio-language Pretraining for Learning General-purpose Audio RepresentationAnnual Meeting of the Association for Computational Linguistics (ACL)Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, et al.2026 · 1 citation
23. Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual TextFindings of the Association for Computational LinguisticsPiyush Singh Pasi2026
24. Rethinking Data Mixing from the Perspective of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuanjian Xu, Tianze Sun, Changwei Xu, et al.2026

95 more papers of our library cite it; they have no page here yet.

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