---
language:
- en
- ar
- nl
- es
- fr
- hi
- it
- pt
- uk
- zh
license: cc-by-4.0
task_categories:
- text-generation
size_categories:
- 100K<n<1M
tags:
- bias
- evaluation
- multilingual
- story-generation
configs:
- config_name: en
  default: true
  data_files:
  - split: train
    path: en/train-*
- config_name: ar
  data_files:
  - split: train
    path: ar/train-*
- config_name: du
  data_files:
  - split: train
    path: du/train-*
- config_name: es
  data_files:
  - split: train
    path: es/train-*
- config_name: fr
  data_files:
  - split: train
    path: fr/train-*
- config_name: hi
  data_files:
  - split: train
    path: hi/train-*
- config_name: it
  data_files:
  - split: train
    path: it/train-*
- config_name: pt
  data_files:
  - split: train
    path: pt/train-*
- config_name: uk
  data_files:
  - split: train
    path: uk/train-*
- config_name: zh
  data_files:
  - split: train
    path: zh/train-*
- config_name: additional_en
  data_files:
  - split: train
    path: additional_en/train-*
- config_name: en_stories
  data_files:
  - split: train
    path: en/stories/eval-*
- config_name: ar_stories
  data_files:
  - split: train
    path: ar/stories/eval-*
- config_name: du_stories
  data_files:
  - split: train
    path: du/stories/eval-*
- config_name: es_stories
  data_files:
  - split: train
    path: es/stories/eval-*
- config_name: fr_stories
  data_files:
  - split: train
    path: fr/stories/eval-*
- config_name: hi_stories
  data_files:
  - split: train
    path: hi/stories/eval-*
- config_name: it_stories
  data_files:
  - split: train
    path: it/stories/eval-*
- config_name: pt_stories
  data_files:
  - split: train
    path: pt/stories/eval-*
- config_name: uk_stories
  data_files:
  - split: train
    path: uk/stories/eval-*
- config_name: zh_stories
  data_files:
  - split: train
    path: zh/stories/eval-*
- config_name: additional_en_stories
  data_files:
  - split: train
    path: additional_en/stories/eval-*
- config_name: human_study
  data_files:
  - split: train
    path: human_study/*.parquet
- config_name: llm_evals
  data_files:
  - split: train
    path: llm_evals/*.parquet
- config_name: associations_by_language
  data_files:
  - split: train
    path: associations_by_language/*.parquet
- config_name: associations_global
  data_files:
  - split: train
    path: associations_global/*.parquet
---

# Multilingual Story-Generation Bias Samples

A **multilingual** evaluation dataset for probing demographic biases in LLM
story generation. Each sample instructs a model to write a ~200-word story
about a character carrying a given demographic attribute value (age, gender,
ethnicity, religion, disability status, immigration status, ...) placed into a
specific life scenario, with the goal of surfacing socio-economic and
demographic biases in the generated narratives.

## Languages and organization

The dataset is organized as **one config per language**, so that users can
load a specific language subset independently:

```python
from datasets import load_dataset

ds_fr = load_dataset("<repo_id>", "fr", split="train")
ds_zh = load_dataset("<repo_id>", "zh", split="train")
```

All language configs share the **same structure**: identical attributes,
attribute values, scenarios, and prompt templates — only the natural-language
content is translated and human-reviewed. This enables direct cross-lingual
comparison of model behavior.

An additional English-only config, `additional_en`, contains scenario-free
prompts (character + attribute without a scenario) used as a control.

## Repository contents

This repository includes the following contents:

### Configs and row counts

| Config          | Language                                    | Rows   |
|-----------------|---------------------------------------------|--------|
| `additional_en` | English (additional prompts, no scenario)   | 237    |
| `ar`            | Arabic                                      | 2,844  |
| `du`            | Dutch                                       | 2,844  |
| `en`            | English                                     | 2,844  |
| `es`            | Spanish                                     | 2,844  |
| `fr`            | French                                      | 2,844  |
| `hi`            | Hindi                                       | 2,844  |
| `it`            | Italian                                     | 2,844  |
| `pt`            | Portuguese                                  | 2,844  |
| `uk`            | Ukrainian                                   | 2,844  |
| `zh`            | Chinese                                     | 2,844  |

**Total rows across configs:** 28,677

### Generated samples - stories

The dataset also includes `*_stories` configs with cleaned model outputs and
attribute extraction results exported our runs.


`<lang>/stories` row counts depend on the run/model selection used for export.

| Config          | Language                                    | Rows   |
|-----------------|---------------------------------------------|--------|
| `en/stories`    | English                                     | 65,412 |
| `ar/stories`    | Arabic                                      | 65,412 |
| `du/stories`    | Dutch                                       | 65,412  |
| `es/stories`    | Spanish                                     | 65,412  |
| `fr/stories`    | French                                      | 65,412  |
| `hi/stories`    | Hindi                                       | 65,412  |
| `it/stories`    | Italian                                     | 65,412  |
| `pt/stories`    | Portuguese                                  | 65,412  |
| `uk/stories`    | Ukrainian                                   | 65,412 |
| `zh/stories`    | Chinese                                     | 65,412  |
| `additional_en/stories` | English (additional prompts, no scenario)   |  	81,765   |

### Analysis and study artifacts

The repository also includes **Parquet** tables for human evaluation, model self-evaluation, and associations listings.

The `human_study/` directory contains structured exports from the **human annotation** on the associations generated by the models:

- **`participants.parquet`** — One row per enrolled participant (or panelist), including fields needed to interpret ratings at the participant level (for example recruitment channel, locale, or study arm), without tying tables to raw platform identifiers beyond what the public release allows.
- **`survey_results.parquet`** — One row per **association judgment**: association id, language, model or condition labels when shown as a factor, ordinal or categorical ratings, optional free text, and timestamps or task metadata when collected.

The `llm_evals/` directory contains the **Consolidated automated evaluation** over the associations generated by the models themselves:

- **`eval_results.parquet`** — One row per **association judgment** aligned with the same identifiers used in the `*_stories`.

The `associations_by_language/` and `associations_global/` directories contain the associations computed with the two aggregation strategies:
- global: all stories generated are taken into account in the statistical analysis. 
- by_language: we repeat the full statistical pipeline independently on each language, only aggregating stories that are generated with the same language.

Results are split into two Parquet files per directory:

- `category_associations.parquet`: contains the associations detected at the attribute level (e.g. age related to parental_status). Quantifies whether the two variables co-vary across stories in the slice using a **full contingency-table** test, **bias-corrected Cramér’s V**, a coarse **effect_category** (`negligible` / `small` / `medium` / `large`, scaled by table size), and a **significant** flag after **multiple-testing correction across compared attributes** (Benjamini–Hochberg in the reference implementation). Rows are keyed by **model** and, for `associations_by_language`, **language**. 
- `value_associations.parquet`: contains the associations detected at the attribute value level (e.g. age=senior related to parental_status=with child). Contains per-cell statistics such as adjusted *p*-value, **observed fraction** within the base row, and **lift** versus expected counts under independence (**FDR** control via Benjamini–Yekutieli on cell *p*-values in the reference pipeline)


> **Note:** Full **contingency tables** may be omitted from the published Parquet to keep artifacts small; primary columns (`association_id`, `model`, `language` where relevant, attributes, *p*-values, `cramer_v`, `effect_category`, `significant`, and value-level statistics) are present. Tables can be reconstructed from story-level extractions if you need the raw counts.

## Schema

Samples follow the [Flare](https://github.com/Giskard-AI/flare) `Sample`
schema. Each row is a self-contained evaluation unit with the following
top-level fields:

- `id` *(str)* — UUID of the sample.
- `module` *(str)* — always `"biases"`.
- `task` *(str)* — always `"story_generation"`.
- `language` *(str)* — ISO language code (`"en"`, `"fr"`, ...).
- `generations` *(list)* — one or more generation specs, each with a
  `messages` list (OpenAI chat format), sampling `params`, and a `metadata`
  dict describing the attribute / scenario / template combination used to
  build the prompt.
- `metadata` *(dict)* — sample-level attribute / scenario / character /
  prompt-template metadata.
- `evaluation` *(dict)* — scorer spec (`biases/attribute_extraction`)
  consumed downstream by [Flare](https://github.com/Giskard-AI/flare).

### Story export schema (`*_stories` configs)

Each row represents one generated output from one model for one sample. Fields:

- `sample_id` *(str)*
- `output_id` *(str)*
- `generator_model` *(str)*
- `language` *(str)*
- `target_attribute` *(str)*
- `target_attribute_value` *(str)*
- `attribute_value_key` *(str)*
- `scenario` *(str | null)*
- `scenario_key` *(str | null)*
- `scenario_group` *(str | null)*
- `character` *(str)*
- `prompt_template` *(str)*
- `user_prompt` *(str)*
- `story` *(str)*
- `extraction_score` *(float | null)*
- `extracted_attributes_json` *(str, JSON-serialized dict)*

To keep story exports lightweight and stable, internal provider payloads and
cost metadata (e.g. `usage`, `cost`, `raw_responses`, `raw_extractions`) are
excluded.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("<repo_id>", "en", split="train")
print(ds[0]["generations"][0]["messages"][0]["content"])
```

To iterate over every language:

```python
from datasets import get_dataset_config_names, load_dataset

for cfg in get_dataset_config_names("<repo_id>"):
    ds = load_dataset("<repo_id>", cfg, split="train")
    # ... evaluate your model on ds ...
```

## Source

Samples were generated by combining manually translated multilingual seeds (attributes, prompt
templates, and scenarios).