--- title: "Who cites CodeNet: A Large-Scale AI for — 69 citing papers" description: "69 papers of the Elamur Research library cite «CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks», found…" canonical: "https://research.elamur.ai/free/en/papers/puri-2021-codenet-large-scale-ai-code/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/puri-2021-codenet-large-scale-ai-code/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [CodeNet: A Large-Scale AI for Code Dataset for…](https://md.research.elamur.ai/free/en/papers/puri-2021-codenet-large-scale-ai-code/) / cited by # Who cites «CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks» 69 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks»](https://md.research.elamur.ai/free/en/papers/puri-2021-codenet-large-scale-ai-code/) 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. [**Show your work: Scratchpads for intermediate computation with language models***2021 · 473 citations*](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/) 3. **[ReACC: A Retrieval-Augmented Code Completion Framework](https://md.research.elamur.ai/free/en/papers/lu-2022-reacc-retrieval-augmented-code-completion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuai Lu, Nan Duan, Hojae Han, et al.*2022 · 125 citations* 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. **[XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://md.research.elamur.ai/free/en/papers/khan-2024-xcodeeval-execution-based-large-scale/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.*2024 · 51 citations* 6. [**xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval arXiv:2303.03004**Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/khan-2023-xcodeeval-large-scale-multilingual-multitask/) 7. **[SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios](https://md.research.elamur.ai/free/en/papers/orel-2026-semeval-2026-task-13-detecting/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Daniil Orel, Dilshod Azizov, Indraneil Paul, et al.*2026 · 41 citations* 8. **[AICD Bench: A Challenging Benchmark for AI-Generated Code Detection](https://md.research.elamur.ai/free/en/papers/orel-2026-aicd-bench-challenging-benchmark-ai/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Daniil Orel, Dilshod Azizov, Indraneil Paul, et al.*2026 · 23 citations* 9. **[Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents](https://md.research.elamur.ai/free/en/papers/wu-2025-embracing-imperfection-simulating-students-diverse/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tao Wu, Jingyuan Chen, Wang Lin, et al.*2025 · 13 citations* 10. **[EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking](https://md.research.elamur.ai/free/en/papers/wei-2025-equibench-benchmarking-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anjiang Wei, Jiannan Cao, Ran Li, et al.*2025 · 8 citations* 11. **[Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation](https://md.research.elamur.ai/free/en/papers/moon-2026-don-t-judge-code-its/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiwon Moon, Yerin Hwang, Dongryeol Lee, et al.*2026 · 5 citations* 12. **[SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification](https://md.research.elamur.ai/free/en/papers/zhou-2026-sactor-llm-driven-correct-idiomatic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyang Zhou, Ziyi Zhang, Haowen Lin, et al.*2026 · 1 citation* 13. **[LLMs are Brittle to Simple Code Transformations: Introducing CETBench – A Benchmark for Code-Equivalence Checking](https://md.research.elamur.ai/free/en/papers/oza-2026-llms-are-brittle-simple-code/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Neeva Oza, Ishaan Govil, Parul Gupta, et al.*2026* 56 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/puri-2021-codenet-large-scale-ai-code/) — CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks