README.md

---
pretty_name: Macaron
tags:
  - benchmark
  - evaluation
  - multilingual
  - multicultural
  - reasoning
  - template-based
task_categories:
  - question-answering
  - text-classification
annotations_creators:
  - expert-generated
language:
  - en
  - am
  - ar
  - zh
  - ka
  - el
  - hi
  - id
  - it
  - ja
  - ky
  - es
  - pt
  - yo
  - tl
  - zu
  - th
  - tr
  
configs:
  - config_name: MCQ
    data_files: "macaron_mcq.csv"
  - config_name: True-False
    data_files: "macaron_truefalse.csv"
  - config_name: Templates
    data_files: "templates_rowshf.csv"
---

# Macaron

**Macaron** is a controlled, human-written benchmark for **multilingual and multicultural reasoning** created with a **template-first** approach.  
Each example is **scenario-aligned** across **English** and a **local language**, enabling controlled comparison of reasoning under culturally grounded premises.

---

## At a glance

| Configuration | Rows | Description |
|--------------|------|-------------|
| MCQ | 1,977 | Bilingual multiple-choice questions (English + local language) |
| True-False | 3,954 | Bilingual verification statements derived from MCQs |
| Templates | 100 | Reusable templates with reasoning and cultural metadata |

> Counting each language-specific instance separately (English + local), the benchmark contains **11,862 evaluation instances**.

---

## Supported tasks

- Multiple-choice question answering
- Binary classification / verification (True/False)

---

## Coverage

Macaron provides controlled coverage across **languages, cultural contexts, reasoning types, and cultural aspects**.  
All instances are **scenario-aligned** across English and a local language.

### Languages and cultural contexts

The benchmark spans **20 cultural contexts**, each paired with **English** and one primary local language.

| Country / Context | Local language |
|------------------|----------------|
| Brazil | Brazilian Portuguese |
| China | Chinese |
| Egypt | Egyptian Arabic |
| Ethiopia | Amharic |
| Georgia | Georgian |
| Greece | Greek |
| India | Hindi |
| Indonesia | Indonesian |
| Italy | Italian |
| Japan | Japanese |
| Kyrgyzstan | Kyrgyz |
| Mexico | Mexican Spanish |
| Morocco | Moroccan Arabic |
| Nigeria | Yoruba |
| Philippines | Tagalog |
| South Africa | Zulu |
| Thailand | Thai |
| Tunisia | Tunisian Arabic |
| Turkey | Turkish |
| Yemen | Yemeni Arabic |

---

### Dataset size by context

Each multiple-choice question (MCQ) produces:
- **1 MCQ row**
- **2 True-False rows**

Each row contains **both English and local-language text**.

| Country / Context | MCQ rows | True-False rows | Evaluation instances (EN + Local) |
|------------------|----------|-----------------|----------------------------------|
| Brazil | 100 | 200 | 600 |
| China | 97 | 194 | 582 |
| Egypt | 99 | 198 | 594 |
| Ethiopia | 98 | 196 | 588 |
| Georgia | 99 | 198 | 594 |
| Greece | 100 | 200 | 600 |
| India | 100 | 200 | 600 |
| Indonesia | 95 | 190 | 570 |
| Italy | 98 | 196 | 588 |
| Japan | 99 | 198 | 594 |
| Kyrgyzstan | 100 | 200 | 600 |
| Mexico | 99 | 198 | 594 |
| Morocco | 100 | 200 | 600 |
| Nigeria | 95 | 190 | 570 |
| Philippines | 99 | 198 | 594 |
| South Africa | 100 | 200 | 600 |
| Thailand | 99 | 198 | 594 |
| Tunisia | 100 | 200 | 600 |
| Turkey | 100 | 200 | 600 |
| Yemen | 100 | 200 | 600 |

---

### Reasoning types

Each template and derived instance is tagged with one or more **reasoning types**.

| Reasoning type | Description |
|---------------|-------------|
| Mathematical Reasoning | Numerical computation and comparison |
| Commonsense Reasoning | Everyday plausibility and typical situations |
| Causal Reasoning | Cause–effect relations |
| Temporal Reasoning | Time, order, calendars |
| Logical Reasoning | Deduction, implication, and analogy |
| Spatial Reasoning | Geographic and spatial relations |
| Multi-hop Reasoning | Composition of two or more inference steps |

---

### Cultural aspects

Templates are tagged with one or more **cultural aspects**, covering **22 domains of everyday life**:

- agriculture
- brands and commerce
- cities and landmarks
- death and funerals
- education
- events and festivals
- famous people
- fashion and media
- folklore and folktales
- food and cuisine
- language and communication
- literature and written works
- music and art
- naming
- objects and units
- politics and governance
- relationships
- social customs
- sports
- time
- transportation
- socio-religious aspects of life

Both `reasoning_category` and `cultural_aspect` fields are **multi-label**, stored as comma-separated strings in the CSV files.

<!-- ### Cultural aspects

Templates are also tagged with one or more **cultural aspects**, covering 22 domains of everyday life.

| Cultural aspect |
|----------------|
| Agriculture |
| Brands and Commerce |
| Cities and Landmarks |
| Death and Funerals |
| Education |
| Events and Festivals |
| Famous People |
| Fashion and Media |
| Folklore and Folktales |
| Food and Cuisine |
| Language and Communication |
| Literature and Written works |
| Music and Art |
| Naming |
| Objects and Units |
| Politics and Governance |
| Relationships |
| Social Customs |
| Sports |
| Time |
| Transportation |
| socio-religious aspects of life |

Both `reasoning_category` and `cultural_aspect` fields are **multi-label**, stored as comma-separated strings in the CSV files.

--- -->

## Intended use

Macaron is intended for:

- Zero-shot and few-shot evaluation of multilingual large language models
- Cross-lingual robustness analysis using scenario-aligned English and local-language inputs
- Diagnostic analysis by reasoning type and cultural domain

Not recommended uses:

- Training and testing on the same benchmark
- Drawing broad conclusions about entire cultures, countries, or languages

---

## How to load

```python
from datasets import load_dataset

ds_mcq = load_dataset("AlaaAhmed2444/Macaron", "MCQ")
ds_tf  = load_dataset("AlaaAhmed2444/Macaron", "True-False")
ds_tpl = load_dataset("AlaaAhmed2444/Macaron", "Templates")
```
---

## Ethical considerations and limitations

- Cultural coverage is necessarily coarse: each cultural context is represented by one primary local language and does not capture within-country diversity or dialect continua.
- The benchmark focuses on controlled reasoning formats (multiple-choice and True/False), which do not reflect open-ended dialogue or interactive reasoning settings.
- Results should not be interpreted as representing full cultural or linguistic diversity, but rather as performance on a controlled, template-based evaluation.

---

## Citation

If you use Macaron, please cite the accompanying paper:

```bibtex
@misc{elsetohy_macaron,
  title  = {Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling},
  author = {Elsetohy, Alaa and Hadhoud, Sama and Wibowo, Haryo Akbarianto and Whitehouse, Chenxi and Winata, Genta Indra and Koto, Fajri and Aji, Alham Fikri},
  note   = {will be updated with arXiv link}
}