Byungju Kim
2024
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
Hyeonwoo Kim
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Gyoungjin Gim
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Yungi Kim
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Jihoo Kim
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Byungju Kim
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Wonseok Lee
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Chanjun Park
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
This study presents a novel learning approach designed to enhance both mathematical reasoning and problem-solving abilities of Large Language Models (LLMs). We focus on integrating the Chain-of-Thought (CoT) and the Program-of-Thought (PoT) learning, hypothesizing that prioritizing the learning of mathematical reasoning ability is helpful for the amplification of problem-solving ability. Thus, the initial learning with CoT is essential for solving challenging mathematical problems. To this end, we propose a sequential learning approach, named SAAS (Solving Ability Amplification Strategy), which strategically transitions from CoT learning to PoT learning. Our empirical study, involving an extensive performance comparison using several benchmarks, demonstrates that our SAAS achieves state-of-the-art (SOTA) performance. The results underscore the effectiveness of our sequential learning approach, marking a significant advancement in the field of mathematical reasoning in LLMs.
Data Driven Approach for Mathematical Problem Solving
Byungju Kim
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Wonseok Lee
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Jaehong Kim
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Jungbin Im
Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024
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.
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Co-authors
- Wonseok Lee 2
- Hyeonwoo Kim 1
- Gyoungjin Gim 1
- Yungi Kim 1
- Jihoo Kim 1
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