@inproceedings{raihan-etal-2025-overview,
    title = "Overview of {BLP}-2025 Task 2: Code Generation in {B}angla",
    author = "Raihan, Nishat  and
      Jawad, Mohammad Anas  and
      Rahman, Md Mezbaur  and
      Ulfat, Noshin  and
      Gupta, Pranav  and
      Rahman, Mehrab Mustafy  and
      Karmaker, Santu  and
      Zampieri, Marcos",
    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-1.31/",
    pages = "373--387",
    ISBN = "979-8-89176-314-2",
    abstract = "This paper presents an overview of the BLP 2025 shared task Code Generation in Bangla, organized with the BLP workshop co-located with AACL. The task evaluates Generative AI systems capable of generating executable Python code from natural language prompts written in Bangla. This is the first shared task to address Bangla code generation. It attracted 152 participants across 63 teams, yielding 488 submissions, with 15 system-description papers. Participating teams employed both proprietary and open-source LLMs, with prevalent strategies including prompt engineering, fine-tuning, and machine translation. The top Pass@1 reached 0.99 on the development phase and 0.95 on the test phase. In this report, we detail the task design, data, and evaluation protocol, and synthesize methodological trends observed across submissions. Notably, we observe that the high performance is not based on single models; rather, a pipeline of multiple AI tools and/or methods."
}