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.
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
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:
@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}
}