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        <title>The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation</title>
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        <namePart type="given">Chung-Chi</namePart>
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        <namePart type="given">Yusuke</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation
%A Chen, Chung-Chi
%A Takamura, Hiroya
%A Kobayashi, Ichiro
%A Miyao, Yusuke
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F chen-etal-2024-impact
%X 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.
%U https://aclanthology.org/2024.naacl-short.53
%P 631-637

Markdown (Informal)

[The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation](https://aclanthology.org/2024.naacl-short.53) (Chen et al., NAACL 2024)

• The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation (Chen et al., NAACL 2024)

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.