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

This work introduces systematic approach for enhancing large language models (LLMs) to address Bangla AI mathematical challenges. Through the assessment of diverse LLM configurations, finetuning 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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Publication Details

Journal
International Journal on Natural Language Computing
Published
2024-12-28
DOI
https://doi.org/10.5121/ijnlc.2024.13604
Primary Topic
Topic Modeling
Type
article
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article

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
International Journal on Natural Language Computing
Topic Modeling
article

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
article en

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, finetuning 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.

International Journal on Natural Language ComputingVol. 13(5/6)
Bangladesh University of Engineering and Technology (BD), Dhaka University of Engineering & Technology (BD)
Openalex Percentile: Top 77%
Topic Modeling
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