@inproceedings{rahman-etal-2025-alphaborno,
    title = "{A}lpha{B}orno at {BLP}-2025 Task 2: Code Generation with Structured Prompts and Execution Feedback",
    author = "Rahman, Mohammad Ashfaq Ur  and
      Shochcho, Muhtasim Ibteda  and
      Fahim, Md",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.banglalp-1.63/",
    pages = "615--623",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper explores various prompting strategies in the BLP-2025 Shared Task 2, utilizing a pipeline that first translates Bangla problem descriptions into English with GPT-4o,then applies techniques like zero-shot, few-shot,chain of thought, synthetic test case integration, and a self-repair loop. We evaluated fourLLMs (GPT-4o, Grok-3, Claude 3.7 Sonnet,and Qwen2.5-Coder 14B). Our findings revealthat while traditional methods like few-shotand chain-of-thought prompting provided inconsistent gains, the integration of explicit unittests delivered a substantial performance boostacross all models. The most effective strategycombined zero-shot prompting with these synthetic tests and a self-repair loop, leading GPT4o to achieve a top Pass@1 score of 72.2{\%}.These results represent the value of using explicit constraints and iterative feedback in codegeneration, offering a solid framework that improves the model{'}s code generation capabilities."
}