--- title: "Who cites Yewen Pu — 53 papers of our library" description: "53 papers of the Elamur Research library cite the works of Yewen Pu, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/yewen-pu/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yewen-pu/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Yewen Pu](https://md.research.elamur.ai/free/en/authors/yewen-pu/) / cited by # Who cites Yewen Pu 54 citations of their works from 53 papers of our library (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← Yewen Pu](https://md.research.elamur.ai/free/en/authors/yewen-pu/) 1. [**Program Synthesis with Large Language Models arXiv:2108.07732**Jacob Austin, Augustus Odena, Maxwell Nye, et al.*2021 · 1,192 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-program-synthesis-large-language-models/) 2. **[CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al.*2022 · 297 citations* 3. **[A Survey of Machine Learning for Big Code and Naturalness](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.*2017 · 167 citations* 4. [**Identifying the Risks of LM Agents with an LM-Emulated Sandbox arXiv:2309.15817**Yangjun Ruan, Honghua Dong, Andrew Wang, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2023-identifying-risks-lm-agents-lm/) 5. **[InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://md.research.elamur.ai/free/en/papers/yang-2023-intercode-standardizing-benchmarking-interactive-coding/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**John Yang, Akshara Prabhakar, Karthik Narasimhan, et al.*2023 · 95 citations* 6. [**Planning with Large Language Models for Code Generation arXiv:2303.05510**Shun Zhang, Zhenfang Chen, Yikang Shen, et al.*2023 · 90 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-planning-large-language-models-code/) 7. **[Large Language Model Instruction Following: A Survey of Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Renze Lou, Kai Zhang, Wenpeng Yin*2024 · 83 citations* 8. **[Synthesizing Structured CAD Models with Equality Saturation and Inverse Transformations](https://md.research.elamur.ai/free/en/papers/nandi-2020-synthesizing-structured-cad-models-equality/) [Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-sigplan-conference-on-programming-language-design-and/)**Chandrakana Nandi, Max Willsey, Adam Anderson, et al.*2020 · 77 citations* 9. [**Large Language Models Are Not Strong Abstract Reasoners arXiv:2305.19555**Gael Gendron, Qiming Bao, Michael Witbrock, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/gendron-2023-large-language-models-strong-abstract/) 10. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations* 11. [**Structural Language Models of Code arXiv:1910.00577**Uri Alon, Roy Sadaka, Omer Levy, et al.*2019 · 55 citations*](https://md.research.elamur.ai/free/en/papers/alon-2019-structural-language-models-code/) 12. **[Execution-based Code Generation using Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, et al.*2023 · 47 citations* 13. **[Fault-Aware Neural Code Rankers](https://md.research.elamur.ai/free/en/papers/inala-2022-fault-aware-neural-code-rankers/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeevana Priya Inala, Chenglong Wang, Mei Yang, et al.*2022 · 39 citations* 14. [**Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions arXiv:2205.14318**Ansong Ni, Jeevana Priya Inala, Chenglong Wang, et al.*2022 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ni-2022-learning-math-reasoning-self-sampled/) 15. **[Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus](https://md.research.elamur.ai/free/en/papers/lee-2024-reasoning-abilities-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Seungpil Lee, Woochang Sim, Donghyeon Shin, et al.*2024 · 38 citations* 16. [**The Surprising Effectiveness of Test-Time Training for Few-Shot Learning arXiv:2411.07279**Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2024-surprising-effectiveness-test-time-training/) 17. [**LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations arXiv:2305.18354**Yudong Xu, Wenhao Li, Pashootan Vaezipoor, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-llms-abstraction-reasoning-corpus-successes/) 18. **[Communicating Natural Programs to Humans and Machines](https://md.research.elamur.ai/free/en/papers/acquaviva-2021-communicating-natural-programs-humans-machines/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samuel Acquaviva, Yewen Pu, Marta Kryven, et al.*2021 · 22 citations* 19. [**Evaluating statistical language models as pragmatic reasoners arXiv:2305.01020**Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/) 20. [**H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark arXiv:2409.01374**Solim LeGris, Wai Keen Vong, Brenden M. Lake, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/legris-2024-h-arc-robust-estimate-human/) 21. [**From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey arXiv:2108.11451**Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, et al.*2021 · 3 citations*](https://md.research.elamur.ai/free/en/papers/marra-2021-statistical-relational-neurosymbolic-artificial/) 32 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/yewen-pu/) — Yewen Pu