--- title: "Who cites Dennis Lee — 40 papers of our library" description: "40 papers of the Elamur Research library cite the works of Dennis Lee, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/dennis-lee/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/dennis-lee/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Dennis Lee](https://md.research.elamur.ai/free/en/authors/dennis-lee/) / cited by # Who cites Dennis Lee 43 citations of their works from 40 papers of our library (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← Dennis Lee](https://md.research.elamur.ai/free/en/authors/dennis-lee/) 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. [**Measuring Mathematical Problem Solving With the MATH Dataset arXiv:2103.03874**Dan Hendrycks, Collin Burns, Saurav Kadavath, et al.*2021 · 2,395 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/) 3. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 992 citations* 4. **[Mathematical Capabilities of ChatGPT](https://md.research.elamur.ai/free/en/papers/frieder-2023-mathematical-capabilities-chatgpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Simon Frieder, Luca Pinchetti, Alexis Chevalier, et al.*2023 · 449 citations* 5. **[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* 6. [**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/) 7. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/) 8. [**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/) 9. [**Teaching Algorithmic Reasoning via In-context Learning arXiv:2211.09066**Hattie Zhou, Azade Nova, Hugo Larochelle, et al.*2022 · 96 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/) 10. [**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/) 11. [**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/) 12. [**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/) 13. [**Learning to Prove Theorems by Learning to Generate Theorems arXiv:2002.07019**Mingzhe Wang, Jia Deng*2020 · 53 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-learning-prove-theorems-learning-generate/) 14. [**Lean-STaR: Learning to Interleave Thinking and Proving arXiv:2407.10040**Haohan Lin, Zhiqing Sun, Sean Welleck, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-lean-star-learning-interleave-thinking/) 15. [**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/) 16. [**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/) 17. [**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/) 18. **[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* 19. [**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/) 20. [**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/) 21. [**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/) 22. [**Teaching Temporal Logics to Neural Networks arXiv:2003.04218**Christopher Hahn, Frederik Schmitt, Jens U. Kreber, et al.*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hahn-2020-teaching-temporal-logics-neural-networks/) 23. [**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/) 24. **[A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers](https://md.research.elamur.ai/free/en/papers/meadows-2024-symbolic-framework-evaluating-mathematical-reasoning/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jordan Meadows, Marco Valentino, Damien Teney, et al.*2024 · 14 citations* 16 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/dennis-lee/) — Dennis Lee