用自修正协作框架提升孟加拉语代码生成效果
BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation
- 双模型协作：编码器生成初稿，评审器基于执行反馈优化
- 在BLP-2025上达到84.00%的Pass@1准确率
- 适合低资源语言代码生成研究者参考
孟加拉语在代码生成领域属于低资源语言，缺乏大规模标注数据集和将自然语言规范转换为可执行程序的工具，导致孟加拉语到代码的生成任务极具挑战性。为此，我们提出BanglaForge框架，通过检索增强的双模型协作与自修正机制，结合上下文学习、基于大模型的翻译、系统化提示工程以及基于执行反馈的迭代优化，实现从孟加拉语函数描述生成代码。其中，编码器生成初始方案，评审器负责提升其鲁棒性。在BLP-2025孟加拉语代码生成基准测试中，BanglaForge取得了84.00%的Pass@1准确率，验证了检索、模型协作与自修正在低资源孟加拉语代码生成中的有效性。
原文摘要 · Abstract (English)
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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