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  license: apache-2.0
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  license: apache-2.0
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+ # Dataset Card for "Calc-math_qa"
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+ ## Summary
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+ This dataset is an instance of math_qa dataset, converted to a simple html-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags:
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+ - gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
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+ - output: An output of the external tool
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+ - result: The final answer of the mathematical problem (a number)
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+
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+
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+ ## Supported Tasks
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+ The dataset is intended for training Chain-of-Thought reasoning **models able to use external tools** to enhance the factuality of their responses.
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+ This dataset presents in-context scenarios where models can out-source the computations in the reasoning chain to a calculator.
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+
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+ ## Construction Process
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+ We took the original math_qa dataset, parsed the nested formulas, linearized them into a sequence (chain) of operations, and replace all advanced
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+ function calls (such as `circle_area`) with explicit elementary operations. We evaluate all the steps in each example and filter out examples if their
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+ evaluation does not match the answer selected as correct in the data with a 5% tolerance. The sequence of steps is then saved in HTML-like language
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+ in `chain` column. We keep the original columns in the dataset for convenience.
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+ You can read more information about this process in our [technical report](https://arxiv.org/abs/2305.15017).
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+ ## Content and Data splits
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+ Content and splits correspond to the original math_qa dataset.
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+ See [mathqa HF dataset](https://huggingface.co/datasets/math_qa) and [official website](https://math-qa.github.io/) for more info.
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+
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+ ## Licence
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+ Apache 2.0, consistently with the original dataset.
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+ ## Cite
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+ If you use this version of dataset in research, please cite the [original MathQA paper](https://arxiv.org/abs/1905.13319), ans also [our technical report](https://arxiv.org/abs/2305.15017) as follows:
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+
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+ ```bibtex
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+ @article{kadlcik2023calcx,
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+ title={Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems},
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+ author={Marek Kadlčík and Michal Štefánik},
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+ year={2023},
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+ eprint={2305.15017},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG}
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+ }
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+ ```