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    <abstract>In this paper, we investigate and introduce a novel Llama-2 based model, fine-tuned with an original dataset designed to mirror real-world mathematical challenges. The dataset was collected through a question-answering platform, incorporating solutions generated by both rule-based solver and question answering, to cover a broad spectrum of mathematical concepts and problem-solving techniques. Experimental results demonstrate significant performance improvements when the models are fine-tuned with our dataset. The results suggest that the integration of contextually rich and diverse problem sets into the training substantially enhances the problem-solving capability of language models across various mathematical domains. This study showcases the critical role of curated educational content in advancing AI research.</abstract>
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%0 Conference Proceedings
%T Data Driven Approach for Mathematical Problem Solving
%A Kim, Byungju
%A Lee, Wonseok
%A Kim, Jaehong
%A Im, Jungbin
%Y Valentino, Marco
%Y Ferreira, Deborah
%Y Thayaparan, Mokanarangan
%Y Freitas, Andre
%S Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F kim-etal-2024-data
%X In this paper, we investigate and introduce a novel Llama-2 based model, fine-tuned with an original dataset designed to mirror real-world mathematical challenges. The dataset was collected through a question-answering platform, incorporating solutions generated by both rule-based solver and question answering, to cover a broad spectrum of mathematical concepts and problem-solving techniques. Experimental results demonstrate significant performance improvements when the models are fine-tuned with our dataset. The results suggest that the integration of contextually rich and diverse problem sets into the training substantially enhances the problem-solving capability of language models across various mathematical domains. This study showcases the critical role of curated educational content in advancing AI research.
%U https://aclanthology.org/2024.mathnlp-1.4
%P 25-34

Markdown (Informal)

[Data Driven Approach for Mathematical Problem Solving](https://aclanthology.org/2024.mathnlp-1.4) (Kim et al., MathNLP-WS 2024)

• Data Driven Approach for Mathematical Problem Solving (Kim et al., MathNLP-WS 2024)

ACL

• Byungju Kim, Wonseok Lee, Jaehong Kim, and Jungbin Im. 2024. Data Driven Approach for Mathematical Problem Solving. In Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024, pages 25–34, Torino, Italia. ELRA and ICCL.