用改进的Qwen模型解决孟加拉数学竞赛题，提升中文外语言数学推理能力
Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving
- 结合提示工程与工具集成推理，增强模型数学推导能力
- 通过量化和超参调优，在竞赛中实现高准确率
- 适合关注多语言数学推理与模型优化的研究者
我们提出一种针对孟加拉语数学问题的创新解决方案，专为DL Sprint 3.0 BUET CSE Fest 2024竞赛设计。方法基于Qwen 2.5系列深度学习模型，通过提示工程、模型量化及工具集成推理（TIR）处理复杂计算。初期测试了多种架构，包括微调的Mistral和量化版Qwen，结合翻译技术、检索增强生成（RAG）与自定义数据集构建进行优化。通过人工调整温度、top-p等超参数，提升模型适应性与准确性。移除RAG并进一步调参后，系统鲁棒性显著增强。结果表明，先进NLP技术在解决孟加拉语数学问题方面具有巨大潜力。
原文摘要 · Abstract (English)
We present an innovative approach for solving mathematical problems in Bengali, developed for the DL Sprint 3.0 BUET CSE Fest 2024 Competition. Our method uses advanced deep learning models, notably the Qwen 2.5 series, with improvements made through prompt engineering, model quantization, and Tool Integrated Reasoning (TIR) to handle complex calculations. Initially, we explored various model architectures, including fine-tuned Mistral and quantized Qwen models, refining them with translation techniques, Retrieval-Augmented Generation (RAG), and custom dataset curation. Manual hyperparameter tuning optimized parameters like temperature and top-p to enhance model adaptability and accuracy. Removal of RAG and parameter adjustments further improved robustness. Our approach highlights the potential of advanced NLP techniques in solving Bengali mathematical problems.
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