BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation

Mahir Labib Dihan; Sadif Ahmed; Md Nafiu Rahman

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arXiv · 2512.19122

BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation

Mahir Labib Dihan·Sadif Ahmed·Md Nafiu Rahman

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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.

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Mahir Labib Dihan, Sadif Ahmed, Md Nafiu Rahman. 2025-12-22. BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation. https://arxiv.org/abs/2512.19122

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