@inproceedings{saadi-etal-2025-nala,
title = "{NALA}{\_}{MAINZ} at {BLP}-2025 Task 2: A Multi-agent Approach for {B}engali Instruction to Python Code Generation",
author = "Saadi, Hossain Shaikh and
Alam, Faria and
Sanz-Guerrero, Mario and
Bui, Minh Duc and
Mager, Manuel and
von der Wense, Katharina",
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-1.61/",
pages = "601--607",
ISBN = "979-8-89176-314-2",
abstract = "This paper presents JGU Mainz{'}s winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a Pass@1 score of 95.4. We also make our code public."
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<abstract>This paper presents JGU Mainz’s winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a Pass@1 score of 95.4. We also make our code public.</abstract>
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%0 Conference Proceedings
%T NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bengali Instruction to Python Code Generation
%A Saadi, Hossain Shaikh
%A Alam, Faria
%A Sanz-Guerrero, Mario
%A Bui, Minh Duc
%A Mager, Manuel
%A von der Wense, Katharina
%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 saadi-etal-2025-nala
%X This paper presents JGU Mainz’s winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a Pass@1 score of 95.4. We also make our code public.
%U https://aclanthology.org/2025.banglalp-1.61/
%P 601-607
Markdown (Informal)
[NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bengali Instruction to Python Code Generation](https://aclanthology.org/2025.banglalp-1.61/) (Saadi et al., BanglaLP 2025)
ACL