@inproceedings{kim-etal-2024-saas,
title = "{SAAS}: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models",
author = "Kim, Hyeonwoo and
Gim, Gyoungjin and
Kim, Yungi and
Kim, Jihoo and
Kim, Byungju and
Lee, Wonseok and
Park, Chanjun",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.15",
doi = "10.18653/v1/2024.emnlp-industry.15",
pages = "186--198",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
%A Kim, Hyeonwoo
%A Gim, Gyoungjin
%A Kim, Yungi
%A Kim, Jihoo
%A Kim, Byungju
%A Lee, Wonseok
%A Park, Chanjun
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F kim-etal-2024-saas
%X 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.
%R 10.18653/v1/2024.emnlp-industry.15
%U https://aclanthology.org/2024.emnlp-industry.15
%U https://doi.org/10.18653/v1/2024.emnlp-industry.15
%P 186-198
Markdown (Informal)
[SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models](https://aclanthology.org/2024.emnlp-industry.15) (Kim et al., EMNLP 2024)
ACL
- Hyeonwoo Kim, Gyoungjin Gim, Yungi Kim, Jihoo Kim, Byungju Kim, Wonseok Lee, and Chanjun Park. 2024. SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 186–198, Miami, Florida, US. Association for Computational Linguistics.