用混合方法提升大模型解孟加拉数学奥赛题能力
End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach
- 结合提示工程、数据增强和迭代推理优化模型
- 在多语言环境下显著提升奥数题解答精度
- 适合关注低资源语言数学AI的研究者
本研究提出系统性方法，提升大型语言模型在处理孟加拉语数学挑战任务中的表现。通过评估多种LLM配置、使用特定数据集进行微调，并引入检索增强生成（RAG），显著增强了模型在多语言环境下的推理准确性。关键发现表明，定制化提示、数据集扩充及迭代推理能有效提升模型在奥数级别数学问题上的效率与性能。
原文摘要 · Abstract (English)
This work introduces systematic approach for enhancing large language models (LLMs) to address Bangla AI mathematical challenges. Through the assessment of diverse LLM configurations, fine-tuning with specific datasets, and the implementation of Retrieval-Augmented Generation (RAG), we enhanced the model's reasoning precision in a multilingual setting. Crucial discoveries indicate that customized prompting, dataset augmentation, and iterative reasoning improve the model's efficiency regarding Olympiad-level mathematical challenges.
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