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TruthfulQA

Paper

Title: TruthfulQA: Measuring How Models Mimic Human Falsehoods Abstract: https://arxiv.org/abs/2109.07958

Homepage: https://github.com/sylinrl/TruthfulQA

Citation

@inproceedings{lin-etal-2022-truthfulqa,
    title = "{T}ruthful{QA}: Measuring How Models Mimic Human Falsehoods",
    author = "Lin, Stephanie  and
      Hilton, Jacob  and
      Evans, Owain",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.229",
    doi = "10.18653/v1/2022.acl-long.229",
    pages = "3214--3252",
}

Groups and Tasks

Groups

  • Not part of a group yet.

Tasks

  • truthfulqa_mc1: Multiple-choice, single answer
  • truthfulqa_mc2: Multiple-choice, multiple answers
  • truthfulqa_gen: Answer generation

Checklist

For adding novel benchmarks/datasets to the library:

  • Is the task an existing benchmark in the literature?
    • Have you referenced the original paper that introduced the task?
    • If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?

If other tasks on this dataset are already supported:

  • Is the "Main" variant of this task clearly denoted?
  • Have you provided a short sentence in a README on what each new variant adds / evaluates?
  • Have you noted which, if any, published evaluation setups are matched by this variant?