README.md

metadata

license: mit
tags:
  - natural-language-understanding
language_creators:
  - expert-generated
  - machine-generated
language:
  - en
multilinguality:
  - multilingual
pretty_name: Fact Completion Benchmark for Text Models
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
  - fill-mask
  - text2text-generation
task_ids:
  - fact-checking

Dataset Card for Fact_Completion

Dataset Description

• Homepage: https://bit.ly/ischool-berkeley-capstone
• Repository: https://github.com/daniel-furman/Capstone
• Paper:
• Leaderboard:
• Point of Contact: Daniel Furman (daniel_furman@berkeley.edu)

Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@misc{calibragpt,
  author = {Shreshta Bhat and Daniel Furman and Tim Schott},
  title = {CalibraGPT: The Search for (Mis)Information in Large Language Models},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/daniel-furman/Capstone}},
}

@misc{dong2022calibrating,
      doi = {10.48550/arXiv.2210.03329},
      title={Calibrating Factual Knowledge in Pretrained Language Models}, 
      author={Qingxiu Dong and Damai Dai and Yifan Song and Jingjing Xu and Zhifang Sui and Lei Li},
      year={2022},
      eprint={2210.03329},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{meng2022massediting,
      doi = {10.48550/arXiv.2210.07229},
      title={Mass-Editing Memory in a Transformer}, 
      author={Kevin Meng and Arnab Sen Sharma and Alex Andonian and Yonatan Belinkov and David Bau},
      year={2022},
      eprint={2210.07229},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

[More Information Needed]