@inproceedings{dihan-etal-2025-teamb2b,
    title = "{T}eam{B}2{B} at {BLP}-2025 Task 2: {B}angla{F}orge: {LLM} Collaboration with Self-Refinement for {B}angla Code Generation",
    author = "Dihan, Mahir Labib  and
      Ahmed, Sadif  and
      Rahman, Md Nafiu",
    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-1.66/",
    pages = "642--655",
    ISBN = "979-8-89176-314-2",
    abstract = "Bangla is a low-resource language for code generation, lacking large-scale annotated datasets and tools to transform natural language specifications into executable programs. This makes Bangla-to-code generation a challenging task requiring innovative solutions. To address this, we introduce BanglaForge, a novel framework for generating code from Bangla function descriptions. BanglaForge leverages a retrieval-augmented dual-model collaboration paradigm with self-refinement, combining in-context learning, llm-based translation, systematic prompt engineering, and iterative self-refinement based on execution feedback, where a coder generates initial solutions and a reviewer enhances them for robustness. On the BLP-2025 Bangla Code Generation benchmark, BanglaForge achieves a competitive Pass@1 accuracy of 84.00{\%}, demonstrating the effectiveness of retrieval, model collaboration, and self-refinement for low-resource Bangla code generation."
}