{"ID":2826135,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19122","arxiv_id":"2512.19122","title":"BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation","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.","short_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 framew...","url_abs":"https://arxiv.org/abs/2512.19122","url_pdf":"https://arxiv.org/pdf/2512.19122v1","authors":"[\"Mahir Labib Dihan\",\"Sadif Ahmed\",\"Md Nafiu Rahman\"]","published":"2025-12-22T07:53:16Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}