JU_NLP at BLP-2025 Task 2: Leveraging Zero-Shot Prompting for Bangla Natural Language to Python Code Generation

AbstractCode synthesis from natural language problem statements has recently gained popularity with the use of large language models (LLMs). Most of the available systems and benchmarks, however, are developed for English or other high-resource languages, and a gap exists for low-resource languages such as Bangla. Addressing the gap, the Bangla Language Processing (BLP) Workshop at AACL-IJCNLP 2025 featured a shared task on Bangla-to-Python code generation. Participants were asked to design systems that consume Bangla problem statements and generate executable Python programs. A benchmark data set of training, development, and test splits was provided, and evaluation utilized the Pass@1 metric through hidden test cases. We present here a system we developed, using the state-of-the-art LLMs through a zero-shot prompting setup. We report outcomes on several models, including variants of GPT-4 and Llama-4, and specify their relative strengths and weaknesses. Our best-performing system, based on GPT-4.1, achieved a Pass@1 score of 78.6% over the test dataset. We address the challenges of Bangla code generation, morphological richness, cross-lingual understanding, and functional correctness, and outline the potential for future work in multilingual program synthesis.