--- title: "Who cites Write, Execute, Assess: Program — 29 citing papers" description: "29 papers of the Elamur Research library cite «Write, Execute, Assess: Program Synthesis with a REPL», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/ellis-2019-write-execute-assess-program-synthesis/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ellis-2019-write-execute-assess-program-synthesis/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Write, Execute, Assess: Program Synthesis with a…](https://md.research.elamur.ai/free/en/papers/ellis-2019-write-execute-assess-program-synthesis/) / cited by # Who cites «Write, Execute, Assess: Program Synthesis with a REPL» 29 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «Write, Execute, Assess: Program Synthesis with a REPL»](https://md.research.elamur.ai/free/en/papers/ellis-2019-write-execute-assess-program-synthesis/) 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. [**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/) 4. **[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* 5. [**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/) 6. **[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* 7. [**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/) 8. **[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* 9. **[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* 10. [**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/) 11. **[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* 12. [**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/) 17 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/ellis-2019-write-execute-assess-program-synthesis/) — Write, Execute, Assess: Program Synthesis with a REPL