@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Overview of BLP-2025 Task 2: Code Generation in Bangla
%A Raihan, Nishat
%A Jawad, Mohammad Anas
%A Rahman, Md Mezbaur
%A Ulfat, Noshin
%A Gupta, Pranav
%A Rahman, Mehrab Mustafy
%A Karmaker, Santu
%A Zampieri, Marcos
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Hassan, Naeemul
%Y Prince, Enamul Hoque
%Y Tasnim, Mohiuddin
%Y Rony, Md Rashad Al Hasan
%Y Rahman, Md Tahmid Rahman
%S Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-314-2
%F raihan-etal-2025-overview
%X 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.
%U https://aclanthology.org/2025.banglalp-1.31/
%P 373-387
Markdown (Informal)
[Overview of BLP-2025 Task 2: Code Generation in Bangla](https://aclanthology.org/2025.banglalp-1.31/) (Raihan et al., BanglaLP 2025)
ACL
- Nishat Raihan, Mohammad Anas Jawad, Md Mezbaur Rahman, Noshin Ulfat, Pranav Gupta, Mehrab Mustafy Rahman, Santu Karmaker, and Marcos Zampieri. 2025. Overview of BLP-2025 Task 2: Code Generation in Bangla. In Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025), pages 373–387, Mumbai, India. Association for Computational Linguistics.