metadata

license: apache-2.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: question
      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: correct_option
      dtype: string
    - name: chain
      dtype: string
    - name: result
      dtype: string
    - name: result_float
      dtype: float64
    - name: options_num
      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: index
      dtype: int64
  splits:
    - name: train
      num_bytes: 20469890
      num_examples: 20868
    - name: validation
      num_bytes: 3041317
      num_examples: 3102
    - name: test
      num_bytes: 1974886
      num_examples: 2029
  download_size: 10584934
  dataset_size: 25486093

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 (a number)

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
correct_option - one of 'a', 'b', 'c', 'd', 'e' - should match with result
chain - solution in the form of step-by-step calculations encoded in simple html-like language. computed from annotated_formula column
result - the numerical result of the problem as a string (can be integer, floating number, fraction, ...)
result_float - the result converted to a float
options_num - same as 'options', but parsed to extract the number from string. This is best-effort only - not all values are (or can be) extracted correctly
rationale - human-annotated free-text reasoning that leads to the correct answer
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
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}
}