The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation
Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi, Yusuke Miyao
Abstract
This paper critically examines the arithmetic capabilities of Large Language Models (LLMs), uncovering significant limitations in their performance. Our research reveals a notable decline in accuracy for complex calculations involving large numbers, with addition and subtraction tasks showing varying degrees of proficiency. Additionally, we challenge the notion that arithmetic is language-independent, finding up to a 10% difference in performance across twenty languages. The study also compares self-verification methods with cross-agent collaborations, showing that a single model often outperforms collaborative approaches in basic arithmetic tasks. These findings suggest a need to reassess the effectiveness of LLMs in tasks requiring numerical accuracy and precision.- Anthology ID:
- 2024.naacl-short.53
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 631–637
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.53
- DOI:
- Cite (ACL):
- Chung-Chi Chen, Hiroya Takamura, Ichiro Kobayashi, and Yusuke Miyao. 2024. The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 631–637, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation (Chen et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.naacl-short.53.pdf