Faria Alam
2025
NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bengali Instruction to Python Code Generation
Hossain Shaikh Saadi
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Faria Alam
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Mario Sanz-Guerrero
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Minh Duc Bui
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Manuel Mager
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Katharina von der Wense
Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
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.