---
title: "Who cites Mingzhe Wang — 62 papers of our library"
description: "62 papers of the Elamur Research library cite the works of Mingzhe Wang, found in their bibliographies; the most cited first."
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---
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# Who cites Mingzhe Wang

70 citations of their works from 62 papers of our library (of the 149,439 whose bibliographies we read) · 40 with a page here, the most cited first · [← Mingzhe Wang](https://md.research.elamur.ai/free/en/authors/mingzhe-wang/)

1. [**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/)
2. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 440 citations*
3. **[ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oyvind Tafjord, Bhavana Dalvi, Peter Clark*2021 · 335 citations*
4. **[LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kaiyu Yang, Aidan M. Swope, Alex Gu, et al.*2023 · 253 citations*
5. [**Generative Language Modeling for Automated Theorem Proving arXiv:2009.03393**Stanislas Polu, Ilya Sutskever*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/)
6. **[Explaining Answers with Entailment Trees](https://md.research.elamur.ai/free/en/papers/dalvi-2021-explaining-answers-entailment-trees/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, et al.*2021 · 211 citations*
7. [**Formal Mathematics Statement Curriculum Learning arXiv:2202.01344**Stanislas Polu, Jesse Michael Han, Kunhao Zheng, et al.*2022 · 133 citations*](https://md.research.elamur.ai/free/en/papers/polu-2022-formal-mathematics-statement-curriculum-learning/)
8. [**Learning to Represent Programs with Graphs arXiv:1711.00740**Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi*2017 · 127 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/)
9. [**DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data arXiv:2405.14333**Huajian Xin, Daya Guo, Zhihong Shao, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-advancing-theorem-proving/)
10. [**GamePad: A Learning Environment for Theorem Proving arXiv:1806.00608**Daniel Huang, Prafulla Dhariwal, Dawn Song, et al.*2018 · 94 citations*](https://md.research.elamur.ai/free/en/papers/huang-2018-gamepad-learning-environment-theorem-proving/)
11. [**Learning to Prove Theorems via Interacting with Proof Assistants arXiv:1905.09381**Kaiyu Yang, Jia Deng*2019 · 90 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-learning-prove-theorems-interacting-proof/)
12. [**Proof Artifact Co-training for Theorem Proving with Language Models arXiv:2102.06203**Jesse Michael Han, Jason Rute, Yuhuai Wu, et al.*2021 · 82 citations*](https://md.research.elamur.ai/free/en/papers/han-2021-proof-artifact-co-training-theorem/)
13. [**LEGO-Prover: Neural Theorem Proving with Growing Libraries arXiv:2310.00656**Haiming Wang, Huajian Xin, Chuanyang Zheng, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-lego-prover-neural-theorem-proving/)
14. [**Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers arXiv:2205.10893**Albert Q. Jiang, Wenda Li, Szymon Tworkowski, et al.*2022 · 79 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-thor-wielding-hammers-integrate-language/)
15. [**HOList: An Environment for Machine Learning of Higher-Order Theorem Proving arXiv:1904.03241**Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, et al.*2019 · 78 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-holist-environment-machine-learning-higher/)
16. [**NaturalProofs: Mathematical Theorem Proving in Natural Language arXiv:2104.01112**Sean Welleck, Jiacheng Liu, Ronan Le Bras, et al.*2021 · 76 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2021-naturalproofs-mathematical-theorem-proving-natural/)
17. [**Premise Order Matters in Reasoning with Large Language Models arXiv:2402.08939**Xinyun Chen, Ryan A. Chi, Xuezhi Wang, et al.*2024 · 58 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-premise-order-matters-reasoning-large/)
18. [**A Survey on Deep Learning for Theorem Proving arXiv:2404.09939**Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-survey-deep-learning-theorem-proving/)
19. **[Forward-Backward Reasoning in Large Language Models for Mathematical Verification](https://md.research.elamur.ai/free/en/papers/jiang-2024-forward-backward-reasoning-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weisen Jiang, Han Shi, Longhui Yu, et al.*2024 · 44 citations*
20. [**STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving arXiv:2502.00212**Kefan Dong, Tengyu Ma*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/dong-2025-stp-self-play-llm-theorem/)
21. [**IsarStep: a Benchmark for High-level Mathematical Reasoning arXiv:2006.09265**Wenda Li, Lei Yu, Yuhuai Wu, et al.*2020 · 43 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-isarstep-benchmark-high-level-mathematical/)
22. [**INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving arXiv:2007.02924**Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba, et al.*2020 · 41 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-int-inequality-benchmark-evaluating-generalization/)
23. **[DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function](https://md.research.elamur.ai/free/en/papers/wang-2023-dt-solver-automated-theorem-proving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haiming Wang, Ye Yuan, Zhengying Liu, et al.*2023 · 40 citations*
24. [**Lean Copilot: Large Language Models as Copilots for Theorem Proving in Lean arXiv:2404.12534**Peiyang Song, Kaiyu Yang, Anima Anandkumar*2024 · 39 citations*](https://md.research.elamur.ai/free/en/papers/song-2024-lean-copilot-large-language-models/)
25. [**LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning arXiv:2101.06223**Yuhuai Wu, Markus Rabe, Wenda Li, et al.*2021 · 39 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-lime-learning-inductive-bias-primitives/)
26. [**Learning to Reason in Large Theories without Imitation arXiv:1905.10501**Kshitij Bansal, Christian Szegedy, Markus N. Rabe, et al.*2019 · 36 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2019-learning-reason-large-theories-without/)
27. [**Magnushammer: A Transformer-Based Approach to Premise Selection arXiv:2303.04488**Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/mikula-2023-magnushammer-transformer-based-approach-premise/)
28. **[Graph Representations for Higher-Order Logic and Theorem Proving](https://md.research.elamur.ai/free/en/papers/paliwal-2019-graph-representations-higher-order-logic/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Aditya Paliwal, Sarah Loos, Markus Rabe, et al.*2019 · 33 citations*
29. **[Property Invariant Embedding for Automated Reasoning](https://md.research.elamur.ai/free/en/papers/olsak-2019-property-invariant-embedding-automated-reasoning/) [Frontiers in artificial intelligence and applications](https://md.research.elamur.ai/free/en/venues/frontiers-in-artificial-intelligence-and-applications/)**Miroslav Olšák, Cezary Kaliszyk, Josef Urban*2019 · 30 citations*
30. [**MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data arXiv:2402.08957 (ICLR 2024 spotlight)**Yinya Huang, Xiaohan Lin, Zhengying Liu, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-mustard-mastering-uniform-synthesis-theorem/)
31. [**TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning arXiv:2102.09756**Minchao Wu, Michael Norrish, Christian Walder, et al.*2021 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-tacticzero-learning-prove-theorems-scratch/)
32. **[Learning Formal Mathematics From Intrinsic Motivation](https://md.research.elamur.ai/free/en/papers/poesia-2024-learning-formal-mathematics-intrinsic-motivation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Gabriel Poesia, David Broman, Nick Haber, et al.*2024 · 23 citations*
33. [**Solving Formal Math Problems by Decomposition and Iterative Reflection arXiv:2507.15225**Yichi Zhou, Jianqiu Zhao, Yongxin Zhang, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-solving-formal-math-problems-decomposition/)
34. [**Guiding High-Performance SAT Solvers with Unsat-Core Predictions arXiv:1903.04671 (International Conference on Theory and Applications of Satisfiability Testing. Springer, Cham, 2019)**Daniel Selsam, Nikolaj Bjørner*2019 · 16 citations*](https://md.research.elamur.ai/free/en/papers/selsam-2019-guiding-high-performance-sat-solvers/)
35. [**Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning arXiv:1811.00796**Mitsuru Kusumoto, Keisuke Yahata, Masahiro Sakai*2018 · 15 citations*](https://md.research.elamur.ai/free/en/papers/kusumoto-2018-automated-theorem-proving-intuitionistic-propositional/)
36. **[Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4](https://md.research.elamur.ai/free/en/papers/aniva-2024-pantograph-machine-machine-interaction-interface/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Leni Aniva, Chuyue Sun, Brando Miranda, et al.*2024 · 14 citations*
37. [**Learning to Prove from Synthetic Theorems arXiv:2006.11259**Eser Aygün, Zafarali Ahmed, Ankit Anand, et al.*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/aygun-2020-learning-prove-synthetic-theorems/)
38. **[Consistent Autoformalization for Constructing Mathematical Libraries](https://md.research.elamur.ai/free/en/papers/zhang-2024-consistent-autoformalization-constructing-mathematical-libraries/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lan Zhang, Xin Quan, Andre Freitas*2024 · 11 citations*
39. **[MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation](https://md.research.elamur.ai/free/en/papers/liang-2025-mps-prover-advancing-stepwise-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhenwen Liang, Linfeng Song, Yang Li, et al.*2025 · 4 citations*
40. **[Neural Wani: Toward Accelerating the Automated Theorem Prover wani for Dependent Type Theory](https://md.research.elamur.ai/free/en/papers/miyagawa-2026-neural-wani-accelerating-automated-theorem/) [Workshop on the Bridges and Gaps between Formal and Computational Linguistics](https://md.research.elamur.ai/free/en/venues/brigap/)**Nanako Miyagawa, Hinari Daido, Daisuke Bekki*2026*

22 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/mingzhe-wang/) — Mingzhe Wang