metadata

license: apache-2.0
tags:
  - natural-language-understanding
language_creators:
  - expert-generated
  - machine-generated
multilinguality:
  - multilingual
pretty_name: Polyglot or Not? Fact-Completion Benchmark
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
  - fill-mask
  - text2text-generation
dataset_info:
  features:
    - name: dataset_id
      dtype: string
    - name: stem
      dtype: string
    - name: 'true'
      dtype: string
    - name: 'false'
      dtype: string
    - name: relation
      dtype: string
    - name: subject
      dtype: string
    - name: object
      dtype: string
  splits:
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      num_examples: 26254
    - name: Spanish
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      num_examples: 18786
    - name: French
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      num_examples: 18395
    - name: Russian
      num_bytes: 659526
      num_examples: 3289
    - name: Portuguese
      num_bytes: 4158146
      num_examples: 22974
    - name: German
      num_bytes: 2611160
      num_examples: 16287
    - name: Italian
      num_bytes: 3709786
      num_examples: 20448
    - name: Ukrainian
      num_bytes: 1868358
      num_examples: 7918
    - name: Polish
      num_bytes: 1683647
      num_examples: 9484
    - name: Romanian
      num_bytes: 2846002
      num_examples: 17568
    - name: Czech
      num_bytes: 1631582
      num_examples: 9427
    - name: Bulgarian
      num_bytes: 4597410
      num_examples: 20577
    - name: Swedish
      num_bytes: 3226502
      num_examples: 21576
    - name: Serbian
      num_bytes: 1327674
      num_examples: 5426
    - name: Hungarian
      num_bytes: 865409
      num_examples: 4650
    - name: Croatian
      num_bytes: 1195097
      num_examples: 7358
    - name: Danish
      num_bytes: 3580458
      num_examples: 23365
    - name: Slovenian
      num_bytes: 1299653
      num_examples: 7873
    - name: Dutch
      num_bytes: 3732795
      num_examples: 22590
    - name: Catalan
      num_bytes: 3319466
      num_examples: 18898
  download_size: 27090222
  dataset_size: 52358225
language:
  - en
  - fr
  - es
  - de
  - uk
  - bg
  - ca
  - da
  - hr
  - hu
  - it
  - nl
  - pl
  - pt
  - ro
  - ru
  - sl
  - sr
  - sv
  - cs

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@berkeley.edu

Dataset Summary

This is the dataset for Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models.

Test Description

Given a factual association such as The capital of France is Paris, we determine whether a model adequately "knows" this information with the following test:

Step 1: prompt the model to predict the likelihood of the token Paris following The Capital of France is
Step 2: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.

If the value from 1 is greater than the value from 2 we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]].

For every foundation model of interest (like LLaMA), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions (results).

We also score monolingual models (like GPT-2) on English-only fact-completion (results).

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{polyglot_or_not,
  author = {Daniel Furman and Tim Schott and Shreshta Bhat},
  title = {Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models},
  year = {2023}
  publisher = {GitHub},
  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}
}