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metadata
dataset_info:
  - config_name: arithmetic_1dc
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configs:
  - config_name: arithmetic_1dc
    data_files:
      - split: validation
        path: arithmetic_1dc/validation-*
  - config_name: arithmetic_2da
    data_files:
      - split: validation
        path: arithmetic_2da/validation-*
  - config_name: arithmetic_2dm
    data_files:
      - split: validation
        path: arithmetic_2dm/validation-*
  - config_name: arithmetic_2ds
    data_files:
      - split: validation
        path: arithmetic_2ds/validation-*
  - config_name: arithmetic_3da
    data_files:
      - split: validation
        path: arithmetic_3da/validation-*
  - config_name: arithmetic_3ds
    data_files:
      - split: validation
        path: arithmetic_3ds/validation-*

Dataset Summary

A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.

Languages

English

Source Data

Obtained from https://github.com/openai/gpt-3/tree/master/data

Citation

@article{brown2020language,
    title={Language Models are Few-Shot Learners},
    author={Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
    year={2020},
    eprint={2005.14165},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}