metadata

license: apache-2.0
configs:
  - config_name: original-splits
    data_files:
      - split: train
        path: original-splits/train-*
      - split: validation
        path: original-splits/validation-*
      - split: test
        path: original-splits/test-*
dataset_info:
  config_name: original-splits
  features:
    - name: id
      dtype: string
    - name: question
      dtype: string
    - name: chain
      dtype: string
    - name: result
      dtype: string
    - name: result_float
      dtype: float64
    - name: question_without_options
      dtype: string
    - name: options
      struct:
        - name: A
          dtype: string
        - name: B
          dtype: string
        - name: C
          dtype: string
        - name: D
          dtype: string
        - name: E
          dtype: string
    - name: annotated_formula
      dtype: string
    - name: linear_formula
      dtype: string
    - name: rationale
      dtype: string
    - name: category
      dtype: string
  splits:
    - name: train
      num_bytes: 25058735
      num_examples: 20868
    - name: validation
      num_bytes: 3722848
      num_examples: 3102
    - name: test
      num_bytes: 2423833
      num_examples: 2029
  download_size: 13928430
  dataset_size: 31205416

Dataset Card for "Calc-math_qa"

Summary

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:

gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
output: An output of the external tool
result: The final answer of the mathematical problem (correct option)

Supported Tasks

The dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.

Construction Process

We took the original math_qa dataset, parsed the nested formulas, linearized them into a sequence (chain) of operations, and replace all advanced function calls (such as circle_area) with explicit elementary operations. We evaluate all the steps in each example and filter out examples if their 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 in the chain column. We keep the original columns in the dataset for convenience.

You can read more information about this process in our technical report.

Content and Data splits

Content and splits correspond to the original math_qa dataset. See mathqa HF dataset and official website for more info.

Columns:

question - th description of a mathematical problem in natural language
options - dictionary with choices 'A' to 'E' as possible solutions
chain - solution in the form of step-by-step calculations encoded in simple html-like language. computed from annotated_formula column
result - the correct option
result_float - the result converted to a float
annotated_formula - human-annotated nested expression that (approximately) evaluates to the selected correct answer
linear_formula - same as annotated_formula, but linearized by original math_qa authors
rationale - human-annotated free-text reasoning that leads to the correct answer
index - index of the example in the original math_qa dataset

Licence

Apache 2.0, consistently with the original dataset.

Cite

If you use this version of dataset in research, please cite the original MathQA paper, and also our technical report as follows:

@article{kadlcik2023calcx,
         title={Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems}, 
         author={Marek Kadlčík and Michal Štefánik},
         year={2023},
         eprint={2305.15017},
         archivePrefix={arXiv},
         primaryClass={cs.LG}
}