End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark:Leveraging Large Language Model Using Integrated Approach

End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark:Leveraging Large Language Model Using Integrated Approach Authors H.M.Shadman Tabib and Jaber Ahmed Deedar, Bangladesh University of Engineering and Technology, Bangladesh Abstract 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 cus- tomized prompting, dataset augmentation, and iterative reasoning improve the model’s efficiency regarding Olympiad-level mathematical challenges. Keywords Large Language Models (LLMs), Fine-Tuning, Bangla AI, Mathematical Reasoning, Retrieval- Augmented Generation (RAG), Multilingual Setting, Customized Prompting, Dataset Augmentation, It- erative Reasoning, Olympiad-Level Challenges.