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

Fuente: arXiv
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Main Authors: Tabib, H. M. Shadman, Deedar, Jaber Ahmed
Format: Preprint
Published: 2025
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author Tabib, H. M. Shadman
Deedar, Jaber Ahmed
author_facet Tabib, H. M. Shadman
Deedar, Jaber Ahmed
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2501_04425
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach
Tabib, H. M. Shadman
Deedar, Jaber Ahmed
Computation and Language
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.
title End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach
topic Computation and Language
url https://arxiv.org/abs/2501.04425