README.md

---
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:
  - name: English
    num_bytes: 3474255
    num_examples: 26254
  - name: Spanish
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    num_examples: 18786
  - name: French
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    num_examples: 18395
  - name: Russian
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    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: 27093258
  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

- **Homepage:** https://bit.ly/ischool-berkeley-capstone
- **Repository:** https://github.com/daniel-furman/Capstone
- **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](https://arxiv.org/abs/2302.13971)), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions ([results](https://github.com/daniel-furman/capstone#multilingual-fact-completion-results)). 
 
 We also score monolingual models (like [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)) on English-only fact-completion ([results](https://github.com/daniel-furman/capstone#english-fact-completion-results)).

## Languages

The dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it,
nl, pl, pt, ro, ru, sl, sr, sv, uk.

## Data Splits

The dataset splits correspond to the 20 languages above. 

## Source Data

We sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts.

## Licensing Information

The dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit. 

## 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}},
}
```

## Bibliography

[1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. "Calibrating Factual Knowledge in Pretrained Language Models". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022).

```
@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}
}
```

[2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. "Mass Editing Memory in a Transformer." arXiv preprint [arXiv:2210.07229][memit] (2022).

```
@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}
}
```