--- title: "Code & programming languages — papers" description: "Code & programming languages: 37 papers of the Elamur Research library, the most cited first." canonical: "https://research.elamur.ai/free/en/topics/code-and-programming/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/topics/code-and-programming/) [home](https://md.research.elamur.ai/free/en/) / [topics](https://md.research.elamur.ai/free/en/topics/) / Code & programming languages # 💻 Code & programming languages 37 papers · page 1 of 2 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/) Programs as text: code generation, program synthesis and the languages programs are written in. **1** [2](https://md.research.elamur.ai/free/en/topics/code-and-programming/page/2/) [next →](https://md.research.elamur.ai/free/en/topics/code-and-programming/page/2/) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/) — Zhangyin Feng, Daya Guo, Duyu Tang, et al. · 2020 · 2 954 citations - [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/) — Tao Yu, Rui Zhang, Kai Yang, et al. · 2018 · 1 582 citations - [Competition-Level Code Generation with AlphaCode](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/) — Yujia Li, David Choi, Junyoung Chung, et al. · 2022 · 1 543 citations - [Program Synthesis with Large Language Models](https://md.research.elamur.ai/free/en/papers/austin-2021-program-synthesis-large-language-models/) — Jacob Austin, Augustus Odena, Maxwell Nye, et al. · 2021 · 1 136 citations - [A Syntactic Neural Model for General-Purpose Code Generation](https://md.research.elamur.ai/free/en/papers/yin-2017-syntactic-neural-model-general-purpose/) — Pengcheng Yin, Graham Neubig · 2017 · 772 citations - [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://md.research.elamur.ai/free/en/papers/guo-2022-unixcoder-unified-cross-modal-pre/) — Daya Guo, Shuai Lu, Nan Duan, et al. · 2022 · 768 citations - [Unified Pre-training for Program Understanding and Generation](https://md.research.elamur.ai/free/en/papers/ahmad-2021-unified-pre-training-program-understanding/) — Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, et al. · 2021 · 746 citations - [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://md.research.elamur.ai/free/en/papers/husain-2020-codesearchnet-challenge-evaluating-state-semantic/) — Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, et al. · 2020 · 647 citations - [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/liu-2023-your-code-generated-chatgpt-really/) — Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, et al. · 2023 · 632 citations - [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://md.research.elamur.ai/free/en/papers/yang-2024-swe-agent-agent-computer-interfaces/) — John Yang, Carlos E. Jimenez, Alexander Wettig, et al. · 2024 · 507 citations - [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://md.research.elamur.ai/free/en/papers/zheng-2023-codegeex-pre-trained-model-code/) — Qinkai Zheng, Xiao Xia, Xu Zou, et al. · 2023 · 462 citations - [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://md.research.elamur.ai/free/en/papers/gao-2023-text-sql-empowered-large-language/) — Dawei Gao, Haibin Wang, Yaliang Li, et al. · 2023 · 461 citations - [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://md.research.elamur.ai/free/en/papers/li-2023-llm-already-serve-database-interface/) — Jinyang Li, Binyuan Hui, Ge Qu, et al. · 2023 · 419 citations - [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://md.research.elamur.ai/free/en/papers/pourreza-2023-din-sql-decomposed-context-learning/) — Mohammadreza Pourreza, Davood Rafiei · 2023 · 397 citations - [A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) — Juyong Jiang, Fan Wang, Jiasi Shen, et al. · 2024 · 366 citations - [Improving Text-to-SQL Evaluation Methodology](https://md.research.elamur.ai/free/en/papers/dollak-2018-improving-text-sql-evaluation-methodology/) — Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, et al. · 2018 · 350 citations - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/) — Daya Guo, Shuo Ren, Shuai Lu, et al. · 2021 · 328 citations - [TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation](https://md.research.elamur.ai/free/en/papers/yu-2018-typesql-knowledge-based-type-aware/) — Tao Yu, Zifan Li, Zilin Zhang, et al. · 2018 · 301 citations - [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/) — Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al. · 2022 · 290 citations - [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://md.research.elamur.ai/free/en/papers/li-2023-resdsql-decoupling-schema-linking-skeleton/) — Haoyang Li, Jing Zhang, Cuiping Li, et al. · 2023 · 277 citations - [Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://md.research.elamur.ai/free/en/papers/bogin-2019-representing-schema-structure-graph-neural/) — Ben Bogin, Jonathan Berant, Matt Gardner · 2019 · 272 citations - [Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://md.research.elamur.ai/free/en/papers/lin-2020-bridging-textual-tabular-data-cross/) — Xi Victoria Lin, Richard Socher, Caiming Xiong · 2020 · 261 citations - [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://md.research.elamur.ai/free/en/papers/yu-2019-cosql-conversational-text-sql-challenge/) — Tao Yu, Rui Zhang, Heyang Er, et al. · 2019 · 261 citations - [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://md.research.elamur.ai/free/en/papers/wang-2020-rat-sql-relation-aware-schema/) — Bailin Wang, Richard Shin, Xiaodong Liu, et al. · 2020 · 239 citations - [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://md.research.elamur.ai/free/en/papers/deepseek-2024-ai-deepseek-coder-v2-breaking-barrier/) — DeepSeek-AI, Qihao Zhu, Daya Guo, et al. · 2024 · 195 citations **1** [2](https://md.research.elamur.ai/free/en/topics/code-and-programming/page/2/) [next →](https://md.research.elamur.ai/free/en/topics/code-and-programming/page/2/) - [← previous topic Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) - [next topic → Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) - [↑ back to topics](https://md.research.elamur.ai/free/en/topics/)