🔗 Who cites «MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning»

57 papers of our library cite it (of the 141,840 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning»

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source SuitesScience China Information SciencesZhe Chen, Weiyun Wang, Hao Tian, et al.2024 · 529 citations
1. Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time ScalingarXiv:2412.05271Zhe Chen, Weiyun Wang, Yue Cao, et al.2024 · 276 citations
   Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language ModelsIEEE Computer Society Conference on Computer Vision and Pattern RecognitionMatt Deitke, Christopher Clark, Sangho Lee, et al.2024 · 184 citations
2. From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction TuningarXiv:2308.12032Ming Li, Yong Zhang, Zhitao Li, et al.2023 · 157 citations
3. InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large ModelarXiv:2401.16420Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.2024 · 92 citations
4. Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-TuningarXiv:2402.00530Ming Li, Yong Zhang, Shwai He, et al.2024 · 60 citations
   Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table TranslationFindings of the Association for Computational LinguisticsSeok Hwan Song, Azher Ahmed Efat, Wallapak Tavanapong2026

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/liu-2024-mmc-advancing-multimodal-chart-understanding/cited-by/