metadata

language:
  - en
license: apache-2.0
size_categories:
  - 10K<n<100K
task_categories:
  - question-answering
pretty_name: AQuA-RAT with Calculator
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: chain
        dtype: string
      - name: result
        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: question_without_options
        dtype: string
    splits:
      - name: train
        num_bytes: 72917721
        num_examples: 94760
      - name: validation
        num_bytes: 212928
        num_examples: 254
      - name: test
        num_bytes: 206180
        num_examples: 254
    download_size: 42057527
    dataset_size: 73336829
  - config_name: original-splits
    features:
      - name: id
        dtype: string
      - name: question
        dtype: string
      - name: chain
        dtype: string
      - name: result
        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: question_without_options
        dtype: string
    splits:
      - name: train
        num_bytes: 74265737
        num_examples: 97467
      - name: validation
        num_bytes: 212928
        num_examples: 254
      - name: test
        num_bytes: 206180
        num_examples: 254
    download_size: 42873590
    dataset_size: 74684845
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
  - 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 Card for Calc-aqua_rat

Summary

This dataset is an instance of AQuA-RAT dataset extended with in-context calls of a sympy calculator.

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

The dataset was constructed automatically by evaluating all candidate calls to a sympy library that were extracted from the originally annotated rationales. The selection of candidates is pivoted by the matching of equals ('=') symbols in the chain, where the left-hand side of the equation is evaluated, and accepted as a correct gadget call, if the result occurs closely on the right-hand side. Therefore, the extraction of calculator calls may inhibit false negatives (where the calculator could have been used but was not), but not any known false positives.

We also perform in-dataset and cross-dataset data-leak detection within the Calc-X collection. Specifically for AQuA-RAT, we removed a few percent of the train split that were near-duplicates with some of the test or validation examples.

A full description of the extraction process can be found in the corresponding parse script,

If you find an issue in the dataset or in the fresh version of the parsing script, we'd be happy if you report it, or create a PR.

Data splits

The dataset with the near-duplicates removed can be loaded in the default config using:

datasets.load_dataset("MU-NLPC/calc-aqua_rat")

If you want the unfiltered version, you can use:

datasets.load_dataset("MU-NLPC/calc-aqua_rat", "original-splits")

Attributes

id: an id of the example
question: A natural language definition of the problem to solve, including the options to choose from
chain: A natural language step-by-step solution with automatically inserted calculator calls and outputs of the sympy calculator
result: The correct option (one of A...E)
options: a dictionary with 5 possible options (A, B, C, D and E), among which one is correct
question_without_options: same as question but without the options inserted

Attributes id, question, chain, and result are present in all datasets in Calc-X collection.

Related work

This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers.

Calc-X collection - datasets for training Calcformers
Calcformers collection - calculator-using models we trained and published on HF
Calc-X and Calcformers paper
Calc-X and Calcformers repo

Here are links to the original dataset:

License

Apache-2.0, consistently with the original aqua-rat dataset.

Cite

If you use this dataset in research, please cite the original AQuA-RAT paper, and Calc-X paper as follows:

@inproceedings{kadlcik-etal-2023-soft,
    title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
    author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
    booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
    month = dec,
    year = "2023",
    address = "Singapore, Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2305.15017",
}