Datasets:

3ebdola
/

wvs2persona

configs:
  - config_name: Andorra
    data_files:
      - split: train
        path: data/andorra/andorra_personas.jsonl
  - config_name: Argentina
    data_files:
      - split: train
        path: data/argentina/argentina_personas.jsonl
  - config_name: Armenia
    data_files:
      - split: train
        path: data/armenia/armenia_personas.jsonl
  - config_name: Australia
    data_files:
      - split: train
        path: data/australia/australia_personas.jsonl
  - config_name: Bangladesh
    data_files:
      - split: train
        path: data/bangladesh/bangladesh_personas.jsonl
  - config_name: Bolivia
    data_files:
      - split: train
        path: data/bolivia/bolivia_personas.jsonl
  - config_name: Brazil
    data_files:
      - split: train
        path: data/brazil/brazil_personas.jsonl
  - config_name: Canada
    data_files:
      - split: train
        path: data/canada/canada_personas.jsonl
  - config_name: Chile
    data_files:
      - split: train
        path: data/chile/chile_personas.jsonl
  - config_name: China
    data_files:
      - split: train
        path: data/china/china_personas.jsonl
  - config_name: Colombia
    data_files:
      - split: train
        path: data/colombia/colombia_personas.jsonl
  - config_name: Cyprus
    data_files:
      - split: train
        path: data/cyprus/cyprus_personas.jsonl
  - config_name: Czechia
    data_files:
      - split: train
        path: data/czechia/czechia_personas.jsonl
  - config_name: Ecuador
    data_files:
      - split: train
        path: data/ecuador/ecuador_personas.jsonl
  - config_name: Egypt
    data_files:
      - split: train
        path: data/egypt/egypt_personas.jsonl
  - config_name: Ethiopia
    data_files:
      - split: train
        path: data/ethiopia/ethiopia_personas.jsonl
  - config_name: Germany
    data_files:
      - split: train
        path: data/germany/germany_personas.jsonl
  - config_name: Great_Britain
    data_files:
      - split: train
        path: data/great_britain/great_britain_personas.jsonl
  - config_name: Greece
    data_files:
      - split: train
        path: data/greece/greece_personas.jsonl
  - config_name: Guatemala
    data_files:
      - split: train
        path: data/guatemala/guatemala_personas.jsonl
  - config_name: Hong_Kong_Sar
    data_files:
      - split: train
        path: data/hong_kong_sar/hong_kong_sar_personas.jsonl
  - config_name: India
    data_files:
      - split: train
        path: data/india/india_personas.jsonl
  - config_name: Indonesia
    data_files:
      - split: train
        path: data/indonesia/indonesia_personas.jsonl
  - config_name: Iran
    data_files:
      - split: train
        path: data/iran/iran_personas.jsonl
  - config_name: Iraq
    data_files:
      - split: train
        path: data/iraq/iraq_personas.jsonl
  - config_name: Japan
    data_files:
      - split: train
        path: data/japan/japan_personas.jsonl
  - config_name: Jordan
    data_files:
      - split: train
        path: data/jordan/jordan_personas.jsonl
  - config_name: Kazakhstan
    data_files:
      - split: train
        path: data/kazakhstan/kazakhstan_personas.jsonl
  - config_name: Kenya
    data_files:
      - split: train
        path: data/kenya/kenya_personas.jsonl
  - config_name: Kyrgyzstan
    data_files:
      - split: train
        path: data/kyrgyzstan/kyrgyzstan_personas.jsonl
  - config_name: Lebanon
    data_files:
      - split: train
        path: data/lebanon/lebanon_personas.jsonl
  - config_name: Libya
    data_files:
      - split: train
        path: data/libya/libya_personas.jsonl
  - config_name: Macau_Sar
    data_files:
      - split: train
        path: data/macau_sar/macau_sar_personas.jsonl
  - config_name: Malaysia
    data_files:
      - split: train
        path: data/malaysia/malaysia_personas.jsonl
  - config_name: Maldives
    data_files:
      - split: train
        path: data/maldives/maldives_personas.jsonl
  - config_name: Mexico
    data_files:
      - split: train
        path: data/mexico/mexico_personas.jsonl
  - config_name: Mongolia
    data_files:
      - split: train
        path: data/mongolia/mongolia_personas.jsonl
  - config_name: Morocco
    data_files:
      - split: train
        path: data/morocco/morocco_personas.jsonl
  - config_name: Myanmar
    data_files:
      - split: train
        path: data/myanmar/myanmar_personas.jsonl
  - config_name: Netherlands
    data_files:
      - split: train
        path: data/netherlands/netherlands_personas.jsonl
  - config_name: New_Zealand
    data_files:
      - split: train
        path: data/new_zealand/new_zealand_personas.jsonl
  - config_name: Nicaragua
    data_files:
      - split: train
        path: data/nicaragua/nicaragua_personas.jsonl
  - config_name: Nigeria
    data_files:
      - split: train
        path: data/nigeria/nigeria_personas.jsonl
  - config_name: Northern_Ireland
    data_files:
      - split: train
        path: data/northern_ireland/northern_ireland_personas.jsonl
  - config_name: Pakistan
    data_files:
      - split: train
        path: data/pakistan/pakistan_personas.jsonl
  - config_name: Peru
    data_files:
      - split: train
        path: data/peru/peru_personas.jsonl
  - config_name: Philippines
    data_files:
      - split: train
        path: data/philippines/philippines_personas.jsonl
  - config_name: Puerto_Rico
    data_files:
      - split: train
        path: data/puerto_rico/puerto_rico_personas.jsonl
  - config_name: Romania
    data_files:
      - split: train
        path: data/romania/romania_personas.jsonl
  - config_name: Russia
    data_files:
      - split: train
        path: data/russia/russia_personas.jsonl
  - config_name: Serbia
    data_files:
      - split: train
        path: data/serbia/serbia_personas.jsonl
  - config_name: Singapore
    data_files:
      - split: train
        path: data/singapore/singapore_personas.jsonl
  - config_name: Slovakia
    data_files:
      - split: train
        path: data/slovakia/slovakia_personas.jsonl
  - config_name: South_Korea
    data_files:
      - split: train
        path: data/south_korea/south_korea_personas.jsonl
  - config_name: Taiwan_Roc
    data_files:
      - split: train
        path: data/taiwan_roc/taiwan_roc_personas.jsonl
  - config_name: Tajikistan
    data_files:
      - split: train
        path: data/tajikistan/tajikistan_personas.jsonl
  - config_name: Thailand
    data_files:
      - split: train
        path: data/thailand/thailand_personas.jsonl
  - config_name: Tunisia
    data_files:
      - split: train
        path: data/tunisia/tunisia_personas.jsonl
  - config_name: Turkey
    data_files:
      - split: train
        path: data/turkey/turkey_personas.jsonl
  - config_name: Ukraine
    data_files:
      - split: train
        path: data/ukraine/ukraine_personas.jsonl
  - config_name: United_States
    data_files:
      - split: train
        path: data/united_states/united_states_personas.jsonl
  - config_name: Uruguay
    data_files:
      - split: train
        path: data/uruguay/uruguay_personas.jsonl
  - config_name: Uzbekistan
    data_files:
      - split: train
        path: data/uzbekistan/uzbekistan_personas.jsonl
  - config_name: Venezuela
    data_files:
      - split: train
        path: data/venezuela/venezuela_personas.jsonl
  - config_name: Vietnam
    data_files:
      - split: train
        path: data/vietnam/vietnam_personas.jsonl
  - config_name: Zimbabwe
    data_files:
      - split: train
        path: data/zimbabwe/zimbabwe_personas.jsonl
pretty_name: wvs2persona
language:
  - en
tags:
  - persona
  - survey
  - world-values-survey
  - sociology
  - culture
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation

WVS2Persona: Parsed World Values Survey (WVS) Wave 7 records into textual personas

wvs2persona dataset overview

Dataset Description

This dataset contains respondent-level persona descriptions derived from the World Values Survey (WVS) Wave 7 core questionnaire.

Each persona corresponds to one individual survey record. These are not cluster centroids, archetypes, or synthetic group summaries. The persona text is a deterministic natural-language rendering of the respondent's answers to the core WVS questionnaire variables only.

On this repo, the dataset is organized by country as subsets/configs. Each subset contains a single train split.

Only the following columns are released in the Hub version:

persona_id
persona

Data Source

The underlying survey source is:

World Values Survey Wave 7

The persona generation pipeline uses only the core questionnaire sections:

Social Values, Attitudes & Stereotypes
Happiness and Well-Being
Social Capital, Trust & Organizational Membership
Economic Values
Corruption
Migration
Security
Postmaterialist Index
Science & Technology
Religious Values
Ethical Values and Norms
Political Interest & Political Participation
Political Culture & Political Regimes
Demographics

No regional modules, contextual variables, or post-core thematic modules are used for the released personas.

Methodology

The methodology followed to create these personas is aligned with the approach described in the following paper:

NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities

ACL Anthology: https://aclanthology.org/2025.emnlp-main.556/
Blog Post: https://elmekki.me/blog/wvs2persona-world-values-survey-personas/

In this dataset:

Each WVS respondent remains a separate record.
Only core questionnaire variables are used.
Structured survey values are decoded into human-readable labels.
A deterministic persona description is generated from the decoded responses.

A concise / summarized persona version, following the NileChat-style approach, will be provided in a future commit.

Dataset Structure

Fields

persona_id: Stable identifier for the persona record.
persona: A full English persona description grounded in the respondent's WVS Wave 7 core-variable responses.

Example

{
    "persona_id": "MAR_504720001",
    "persona": "This person lives in Morocco. She is female, was born in 1969, and is 52 years old. ..."
}

Loading the Dataset

from datasets import load_dataset

REPO_ID = "3ebdola/wvs2persona"

ds_morocco = load_dataset(REPO_ID, "Morocco", split="train")
print(ds_morocco)
print(ds_morocco[0])

Another example:

from datasets import load_dataset

REPO_ID = "3ebdola/wvs2persona"

ds_egypt = load_dataset(REPO_ID, "Egypt", split="train")
print(ds_egypt[0]["persona_id"])
print(ds_egypt[0]["persona"])

Subsets

The dataset currently includes 66 country subsets and 97,220 personas in total.

Subset list and row counts

Subset	Rows
Andorra	1004
Argentina	1003
Armenia	1223
Australia	1813
Bangladesh	1200
Bolivia	2067
Brazil	1762
Canada	4018
Chile	1000
China	3036
Colombia	1520
Cyprus	1000
Czechia	1200
Ecuador	1200
Egypt	1200
Ethiopia	1230
Germany	1528
Great Britain	2609
Greece	1200
Guatemala	1229
Hong Kong SAR	2075
India	1692
Indonesia	3200
Iran	1499
Iraq	1200
Japan	1353
Jordan	1203
Kazakhstan	1276
Kenya	1266
Kyrgyzstan	1200
Lebanon	1200
Libya	1196
Macau SAR	1023
Malaysia	1313
Maldives	1039
Mexico	1741
Mongolia	1638
Morocco	1200
Myanmar	1200
Netherlands	2145
New Zealand	1057
Nicaragua	1200
Nigeria	1237
Northern Ireland	447
Pakistan	1995
Peru	1400
Philippines	1200
Puerto Rico	1127
Romania	1257
Russia	1810
Serbia	1046
Singapore	2012
Slovakia	1200
South Korea	1245
Taiwan ROC	1223
Tajikistan	1200
Thailand	1500
Tunisia	1208
Turkey	2415
Ukraine	1289
United States	2596
Uruguay	1000
Uzbekistan	1250
Venezuela	1190
Vietnam	1200
Zimbabwe	1215

Intended Use

This dataset can be useful for:

persona-based prompting and conditioning
culture-aware or country-aware LLM experimentation
evaluation of value-sensitive or socially grounded generation
retrieval and few-shot selection by country-specific persona text
downstream summarization or compression of long persona descriptions

Limitations

The personas are generated textual summaries, not verbatim respondent statements.
They are grounded in survey answers, but should not be treated as complete biographies.
The released text is in English, even when the original respondents come from non-English-speaking countries.
Persona descriptions may reflect survey instrument limitations, response noise, or country-specific coding artifacts.
Only the core WVS variables are used in this release.

Citation

If you use this dataset, please cite the following paper:

@inproceedings{el-mekki-etal-2025-nilechat,
    title = "{N}ile{C}hat: Towards Linguistically Diverse and Culturally Aware {LLM}s for Local Communities",
    author = "El Mekki, Abdellah  and
      Atou, Houdaifa  and
      Nacar, Omer  and
      Shehata, Shady  and
      Abdul-Mageed, Muhammad",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.556/",
    doi = "10.18653/v1/2025.emnlp-main.556",
    pages = "10967--10991",
    ISBN = "979-8-89176-332-6"
}