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

H. M. Shadman Tabib, Jaber Ahmed Deedar
1/8/2025
cs.CL

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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 customized prompting, dataset augmentation, and iterative reasoning improve the model's efficiency regarding Olympiad-level mathematical challenges.

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Cite this paper

@article{tabib2025endtoend,
  title  = {End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach},
  author = {H. M. Shadman Tabib and Jaber Ahmed Deedar},
  year   = {2025},
  eprint = {2501.04425},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.04425v1}
}

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