Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems

Marek Kadlčík, Michal Štefánik, Ondrej Sotolar, Vlastimil Martinek


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.
Anthology ID:
2023.emnlp-main.742
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12101–12108
Language:
URL:
https://aclanthology.org/2023.emnlp-main.742
DOI:
10.18653/v1/2023.emnlp-main.742
Bibkey:
Cite (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.
Cite (Informal):
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems (Kadlčík et al., EMNLP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-main.742.pdf
Video:
 https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-main.742.mp4