--- title: "Who cites SuperGlue: Learning Feature — 91 citing papers" description: "91 papers of the Elamur Research library cite «SuperGlue: Learning Feature Matching with Graph Neural Networks», found in their…" canonical: "https://research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [SuperGlue: Learning Feature Matching with Graph…](https://md.research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/) / cited by # Who cites «SuperGlue: Learning Feature Matching with Graph Neural Networks» 91 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · [← back to «SuperGlue: Learning Feature Matching with Graph Neural Networks»](https://md.research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/) 1. [**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/) 2. **[ScandEval: A Benchmark for Scandinavian Natural Language Processing](https://md.research.elamur.ai/free/en/papers/nielsen-2023-scandeval-benchmark-scandinavian-natural-language/) [Nordic Conference on Computational Linguistics (NoDaLiDa)](https://md.research.elamur.ai/free/en/venues/nodalida/)**Dan Nielsen*2023 · 73 citations* 3. [**We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? arXiv:2407.01284**Runqi Qiao, Qiuna Tan, Guanting Dong, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2024-math-large-multimodal-model-achieve/) 4. [**RegMix: Data Mixture as Regression for Language Model Pre-training arXiv:2407.01492**Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-regmix-data-mixture-regression-language/) 5. [**SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark arXiv:2307.15020**Liang Xu, Anqi Li, Lei Zhu, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-superclue-comprehensive-chinese-large/) 6. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/) 7. **[MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/zhao-2025-mmlu-cf-contamination-free-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qihao Zhao, Yangyu Huang, Tengchao Lv, et al.*2025 · 21 citations* 8. [**E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models arXiv:2401.15927**Jinchang Hou, Chang Ao, Haihong Wu, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/hou-2024-e-eval-comprehensive-chinese/) 9. **[Batch-wise Convergent Pre-training: Step-by-Step Learning Inspired by Child Language Development](https://md.research.elamur.ai/free/en/papers/yoshida-2025-batch-wise-convergent-pre-training/) [BabyLM Workshop](https://md.research.elamur.ai/free/en/venues/babylm/)**Ko Yoshida, Daiki Shiono, Kai Sato, et al.*2025 · 1 citation* 10. **[Rethinking Data Mixing from the Perspective of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-rethinking-data-mixing-perspective-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanjian Xu, Tianze Sun, Changwei Xu, et al.*2026* 11. **[Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models](https://md.research.elamur.ai/free/en/papers/liu-2026-revisiting-pain-neck-semantic-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Liu, Hongming Li, Melissa Xiaohui Qin, et al.*2026* 80 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/) — SuperGlue: Learning Feature Matching with Graph Neural Networks