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        <title>Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems</title>
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        <namePart type="given">Marek</namePart>
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    <name type="personal">
        <namePart type="given">Michal</namePart>
        <namePart type="family">Štefánik</namePart>
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        <namePart type="given">Ondrej</namePart>
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    <name type="personal">
        <namePart type="given">Vlastimil</namePart>
        <namePart type="family">Martinek</namePart>
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            <namePart type="given">Juan</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <abstract>Despite outstanding performance in many tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. We address this deficiency by creating Calc-X, a collection of datasets that demonstrates the appropriate use of a calculator in reasoning chains. Calc-X is suitable for teaching language models to offload computations to a symbolic system. We survey and unify several existing chain-of-thought datasets into a proposed format, resulting in a standard collection of over 300,000 samples requiring arithmetic reasoning. Finally, we use the new Calc-X collection to train open-source calculator-using models and show that these models approximately double the accuracy of generating correct results compared to vanilla language model baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.742</identifier>
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        <date>2023-12</date>
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            <start>12101</start>
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%0 Conference Proceedings
%T Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems
%A Kadlčík, Marek
%A Štefánik, Michal
%A Sotolar, Ondrej
%A Martinek, Vlastimil
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kadlcik-etal-2023-calc
%X Despite outstanding performance in many tasks, language models are notoriously inclined to make factual errors in tasks requiring arithmetic computation. We address this deficiency by creating Calc-X, a collection of datasets that demonstrates the appropriate use of a calculator in reasoning chains. Calc-X is suitable for teaching language models to offload computations to a symbolic system. We survey and unify several existing chain-of-thought datasets into a proposed format, resulting in a standard collection of over 300,000 samples requiring arithmetic reasoning. Finally, we use the new Calc-X collection to train open-source calculator-using models and show that these models approximately double the accuracy of generating correct results compared to vanilla language model baselines.
%R 10.18653/v1/2023.emnlp-main.742
%U https://aclanthology.org/2023.emnlp-main.742
%U https://doi.org/10.18653/v1/2023.emnlp-main.742
%P 12101-12108

Markdown (Informal)

[Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems](https://aclanthology.org/2023.emnlp-main.742) (Kadlčík et al., EMNLP 2023)

• Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems (Kadlčík et al., EMNLP 2023)

ACL

• Marek Kadlčík, Michal Štefánik, Ondrej Sotolar, and Vlastimil Martinek. 2023. Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12101–12108, Singapore. Association for Computational Linguistics.