🧑‍🔬 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

1. Program Synthesis with Large Language ModelsarXiv:2108.07732Jacob Austin, Augustus Odena, Maxwell Nye, et al.2021 · 1,192 citations
2. CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement LearningConference on Neural Information Processing Systems (NeurIPS)Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al.2022 · 297 citations
3. A Survey of Machine Learning for Big Code and NaturalnessACM Computing SurveysMiltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.2017 · 167 citations
4. Identifying the Risks of LM Agents with an LM-Emulated SandboxarXiv:2309.15817Yangjun Ruan, Honghua Dong, Andrew Wang, et al.2023 · 96 citations
5. InterCode: Standardizing and Benchmarking Interactive Coding with Execution FeedbackConference on Neural Information Processing Systems (NeurIPS)John Yang, Akshara Prabhakar, Karthik Narasimhan, et al.2023 · 95 citations
6. Planning with Large Language Models for Code GenerationarXiv:2303.05510Shun Zhang, Zhenfang Chen, Yikang Shen, et al.2023 · 90 citations
7. Large Language Model Instruction Following: A Survey of Progresses and ChallengesComputational LinguisticsRenze Lou, Kai Zhang, Wenpeng Yin2024 · 83 citations
8. Synthesizing Structured CAD Models with Equality Saturation and Inverse TransformationsProceedings of the ACM SIGPLAN Conference on Programming Language Design and ImplementationChandrakana Nandi, Max Willsey, Adam Anderson, et al.2020 · 77 citations
9. Large Language Models Are Not Strong Abstract ReasonersarXiv:2305.19555Gael Gendron, Qiming Bao, Michael Witbrock, et al.2023 · 61 citations
10. Parsel: Algorithmic Reasoning with Language Models by Composing DecompositionsConference on Neural Information Processing Systems (NeurIPS)Eric Zelikman, Qian Huang, Gabriel Poesia, et al.2023 · 56 citations
11. Structural Language Models of CodearXiv:1910.00577Uri Alon, Roy Sadaka, Omer Levy, et al.2019 · 55 citations
12. Execution-based Code Generation using Deep Reinforcement LearningTransactions on Machine Learning ResearchParshin Shojaee, Aneesh Jain, Sindhu Tipirneni, et al.2023 · 47 citations
13. Fault-Aware Neural Code RankersConference on Neural Information Processing Systems (NeurIPS)Jeevana Priya Inala, Chenglong Wang, Mei Yang, et al.2022 · 39 citations
14. Learning Math Reasoning from Self-Sampled Correct and Partially-Correct SolutionsarXiv:2205.14318Ansong Ni, Jeevana Priya Inala, Chenglong Wang, et al.2022 · 39 citations
15. Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning CorpusACM Transactions on Intelligent Systems and TechnologySeungpil Lee, Woochang Sim, Donghyeon Shin, et al.2024 · 38 citations
16. The Surprising Effectiveness of Test-Time Training for Few-Shot LearningarXiv:2411.07279Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.2024 · 36 citations
17. LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based RepresentationsarXiv:2305.18354Yudong Xu, Wenhao Li, Pashootan Vaezipoor, et al.2023 · 32 citations
18. Communicating Natural Programs to Humans and MachinesConference on Neural Information Processing Systems (NeurIPS)Samuel Acquaviva, Yewen Pu, Marta Kryven, et al.2021 · 22 citations
19. Evaluating statistical language models as pragmatic reasonersarXiv:2305.01020Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.2023 · 18 citations
20. H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus BenchmarkarXiv:2409.01374Solim LeGris, Wai Keen Vong, Brenden M. Lake, et al.2024 · 12 citations
21. From Statistical Relational to Neurosymbolic Artificial Intelligence: a SurveyarXiv:2108.11451Giuseppe Marra, Sebastijan Dumančić, Robin Manhaeve, et al.2021 · 3 citations

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

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