{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pangu-coder-program-synthesis-with-function","title":"PanGu-Coder: Program Synthesis with Function-Level Language Modeling","arxiv_id":"2207.11280","date":"2022-07-22","proceeding":null,"authors":["Fenia Christopoulou","Gerasimos Lampouras","Milan Gritta","Guchun Zhang","Yinpeng Guo","Zhongqi Li","Qi Zhang","Meng Xiao","Bo Shen","Lin Li","Hao Yu","Li Yan","Pingyi Zhou","Xin Wang","Yuchi Ma","Ignacio Iacobacci","Yasheng Wang","Guangtai Liang","Jiansheng Wei","Xin Jiang","Qianxiang Wang","Qun Liu"],"abstract":"We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solutions given a natural language problem description. We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language Modelling (CLM) to pre-train on raw programming language data, while the second stage uses a combination of Causal Language Modelling and Masked Language Modelling (MLM) training objectives that focus on the downstream task of text-to-code generation and train on loosely curated pairs of natural language program definitions and code functions. Finally, we discuss PanGu-Coder-FT, which is fine-tuned on a combination of competitive programming problems and code with continuous integration tests. We evaluate PanGu-Coder with a focus on whether it generates functionally correct programs and demonstrate that it achieves equivalent or better performance than similarly sized models, such as CodeX, while attending a smaller context window and training on less data.","url_abs":"https://arxiv.org/abs/2207.11280v1","url_pdf":"https://arxiv.org/pdf/2207.11280v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pangu-coder-program-synthesis-with-function","repo_url":"https://github.com/MindSpore-paper-code-2/code399/tree/main/Pangu_alpha","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"program-synthesis","task_name":"Program Synthesis"},{"task_slug":"text-to-code-generation","task_name":"Text-to-Code Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.11280","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}