Who cites Mark Harman
- Code Llama: Open Foundation Models for CodearXiv:2308.12950Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.2024 · 1,430 citations
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code GenerationConference on Neural Information Processing Systems (NeurIPS)Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, et al.2023 · 648 citations
- Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating CodeIEEE/ACM International Conference on Software Engineering (ICSE)Rangeet Pan, Ali Reza Ibrahimzada, Rahul Krishna, et al.2024 · 156 citations
- Planning with Large Language Models for Code GenerationarXiv:2303.05510Shun Zhang, Zhenfang Chen, Yikang Shen, et al.2023 · 90 citations
- Language Models Can Teach Themselves to Program BetterarXiv:2207.14502Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai2023 · 54 citations
- XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and RetrievalAnnual Meeting of the Association for Computational Linguistics (ACL)Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.2024 · 51 citations
- xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and RetrievalarXiv:2303.03004Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.2023 · 42 citations
- NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User QueriesFindings of the Association for Computational LinguisticsShudan Zhang, Hanlin Zhao, Xiao Liu, et al.2024 · 17 citations
- Bootstrapping Code Translation with Weighted Multilanguage ExplorationAnnual Meeting of the Association for Computational Linguistics (ACL)Yuhan Wu, Huan Zhang, Wei Cheng, et al.2026
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