@inproceedings{shahrier-etal-2025-cuet,
    title = "{CUET}{\_}{E}xpelliarmus at {BLP}2025 Task 2: Leveraging Instruction Translation and Refinement for {B}angla-to-Python Code Generation with Open-Source {LLM}s",
    author = "Shahrier, Md Kaf  and
      Rashid, Suhana Binta  and
      Ali Taher, Hasan Mesbaul  and
      Hoque, Mohammed Moshiul",
    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-1.62/",
    pages = "608--614",
    ISBN = "979-8-89176-314-2",
    abstract = "Large language models (LLMs) have recently shown strong performance in generating code from natural language prompts. However, current benchmarks are primarily focused on English overlooking low-resource languages like Bangla. This creates a critical research gap since there are no well established resources or systematic evaluations for code generation from Bangla instruction. To address the gap, we present a system that generates executable Python code from Bangla instructions. We design a two-stage pipeline where the Bangla instructions are first translated and refined into clear English version to reduce ambiguity and then the python code is generated from the refined instructions with iterative error-correction. For both instruction refinement and code generation we used the open-source GPT-20B OSS model. On the official test set our system achieves competitive results. We also analyze common errors like unclear instruction, logical mistakes, runtime issues and the need for external knowledge beyond the model{'}s training. Overall, our findings show that a simple translation{--}refinement pipeline can be an effective and low-cost approach for code generation in low-resource languages."
}