Math Problem Solving: Enhancing Large Language Models with Semantically Rich Symbolic Variables

Ali Emre Narin


Abstract
The advent of Large Language Models (LLMs) based on the Transformer architecture has led to remarkable advancements in various domains, including reasoning tasks. However, accurately assessing the performance of Large Language Models, particularly in the reasoning domain, remains a challenge. In this paper, we propose the Semantically Rich Variable Substitution Method (SemRiVas) as an enhancement to existing symbolic methodologies for evaluating LLMs on Mathematical Word Problems (MWPs). Unlike previous approaches that utilize generic symbols for variable substitution, SemRiVas employs descriptive variable names, aiming to improve the problem-solving abilities of LLMs. Our method aims to eliminate the need for LLMs to possess programming proficiency and perform arithmetic operations, to be universally applicable. Our experimental results demonstrate the superior accuracy of SemRiVas compared to prior symbolic methods, particularly in resolving longer and more complex MWP questions. However, LLMs’ performance with SemRiVas and symbolic methods that utilize one-character variables still falls short compared to notable techniques like CoT and PaL.
Anthology ID:
2024.mathnlp-1.3
Volume:
Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Marco Valentino, Deborah Ferreira, Mokanarangan Thayaparan, Andre Freitas
Venues:
MathNLP | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
19–24
Language:
URL:
https://aclanthology.org/2024.mathnlp-1.3
DOI:
Bibkey:
Cite (ACL):
Ali Emre Narin. 2024. Math Problem Solving: Enhancing Large Language Models with Semantically Rich Symbolic Variables. In Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024, pages 19–24, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Math Problem Solving: Enhancing Large Language Models with Semantically Rich Symbolic Variables (Narin, MathNLP-WS 2024)
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PDF:
https://aclanthology.org/2024.mathnlp-1.3.pdf