---
license: cc-by-nc-sa-4.0
task_categories:
  - question-answering
language:
  - ar
  - asm
  - bn
  - en
  - hi
  - ne
  - tr
tags:
  - question-answering
  - cultural-aligned
pretty_name: 'MultiNativQA -- Multilingual Native and Culturally Aligned QA'
size_categories:
  - 10K<n<100K
dataset_info:
  - config_name: Arabic
    splits:
      - name: train
        num_examples: 3649
      - name: dev
        num_examples: 492
      - name: test
        num_examples: 988
  - config_name: Assamese
    splits:
      - name: train
        num_examples: 1131
      - name: dev
        num_examples: 157
      - name: test
        num_examples: 545
  - config_name: Bangla-BD
    splits:
      - name: train
        num_examples: 7018
      - name: dev
        num_examples: 953
      - name: test
        num_examples: 1521
  - config_name: Bangla-IN
    splits:
      - name: train
        num_examples: 6891
      - name: dev
        num_examples: 930
      - name: test
        num_examples: 2146
  - config_name: English-BD
    splits:
      - name: train
        num_examples: 4761
      - name: dev
        num_examples: 656
      - name: test
        num_examples: 1113
  - config_name: English-QA
    splits:
      - name: train
        num_examples: 8212
      - name: dev
        num_examples: 1164
      - name: test
        num_examples: 2322
  - config_name: Hindi
    splits:  
      - name: train
        num_examples: 9288
      - name: dev
        num_examples: 1286
      - name: test
        num_examples: 2745
  - config_name: Nepali
    splits:
      - name: test
        num_examples: 561
  - config_name: Turkish
    splits:
      - name: train
        num_examples: 3527
      - name: dev
        num_examples: 483
      - name: test
        num_examples:  1218
configs:
  - config_name: arabic_qa
    data_files:
      - split: train
        path: arabic_qa/NativQA_ar_msa_qa_train.json
      - split: dev
        path: arabic_qa/NativQA_ar_msa_qa_dev.json
      - split: test
        path: arabic_qa/NativQA_ar_msa_qa_test.json
  - config_name: assamese_in
    data_files:
      - split: train
        path: assamese_in/NativQA_asm_NA_in_train.json
      - split: dev
        path: assamese_in/NativQA_asm_NA_in_dev.json
      - split: test
        path: assamese_in/NativQA_asm_NA_in_test.json
  - config_name: bangla_bd
    data_files:
      - split: train
        path: bangla_bd/NativQA_bn_scb_bd_train.json
      - split: dev
        path: bangla_bd/NativQA_bn_scb_bd_dev.json
      - split: test
        path: bangla_bd/NativQA_bn_scb_bd_test.json
  - config_name: bangla_in
    data_files:
      - split: train
        path: bangla_in/NativQA_bn_scb_in_train.json
      - split: dev
        path: bangla_in/NativQA_bn_scb_in_dev.json
      - split: test
        path: bangla_in/NativQA_bn_scb_in_test.json
  - config_name: english_bd
    data_files:
      - split: train
        path: english_bd/NativQA_en_NA_bd_train.json
      - split: dev
        path: english_bd/NativQA_en_NA_bd_dev.json
      - split: test
        path: english_bd/NativQA_en_NA_bd_test.json
  - config_name: english_qa
    data_files:
      - split: train
        path: english_qa/NativQA_en_NA_qa_train.json
      - split: dev
        path: english_qa/NativQA_en_NA_qa_dev.json
      - split: test
        path: english_qa/NativQA_en_NA_qa_test.json
  - config_name: hindi_in
    data_files:
      - split: train
        path: hindi_in/NativQA_hi_NA_in_train.json
      - split: dev
        path: hindi_in/NativQA_hi_NA_in_dev.json
      - split: test
        path: hindi_in/NativQA_hi_NA_in_test.json
  - config_name: nepali_np
    data_files:
      - split: test
        path: nepali_np/NativQA_ne_NA_np_test.json
  - config_name: turkish_tr
    data_files:
      - split: train
        path: turkish_tr/NativQA_tr_NA_tr_train.json
      - split: dev
        path: turkish_tr/NativQA_tr_NA_tr_dev.json
      - split: test
        path: turkish_tr/NativQA_tr_NA_tr_test.json
---

# MultiNativQA: Multilingual Culturally-Aligned Natural Queries For LLMs

### Overview
The **MultiNativQA** dataset is a multilingual, native, and culturally aligned question-answering resource. It spans 7 languages, ranging from high- to extremely low-resource, and covers 9 different locations/cities. To capture linguistic diversity, the dataset includes several dialects for dialect-rich languages like Arabic. In addition to Modern Standard Arabic (MSA), **MultiNativQA** features six Arabic dialects — *Egyptian, Jordanian, Khaliji, Sudanese, Tunisian*, and *Yemeni*.

The dataset also provides two linguistic variations of Bangla, reflecting differences between speakers in *Bangladesh* and *West Bengal, India*. Additionally, **MultiNativQA** includes English queries from *Dhaka* and *Doha*, where English is commonly used as a second language, as well as from *New York, USA*.

The QA pairs in this dataset cover 18 diverse topics, including: *Animals, Business, Clothing, Education, Events, Food & Drinks, General, Geography, Immigration, Language, Literature, Names & Persons, Plants, Religion, Sports & Games, Tradition, Travel*, and *Weather*.

**MultiNativQA** is designed to evaluate and fine-tune large language models (LLMs) for long-form question answering while assessing their cultural adaptability and understanding.

### Directory Structure (JSON files only)
The dataset is organized into directories based on language and region. Each directory contains JSON files for the train, development, and test sets, with the exception of Nepali, which consists of only a test set.

- `arabic_qa/`
    - `NativQA_ar_msa_qa_dev.json`
    - `NativQA_ar_msa_qa_test.json`
    - `NativQA_ar_msa_qa_train.json`
- `assamese_in/`
    - `NativQA_asm_NA_in_dev.json`
    - `NativQA_asm_NA_in_test.json`
    - `NativQA_asm_NA_in_train.json`
- `bangla_bd/`
    - `NativQA_bn_scb_bd_dev.json`
    - `NativQA_bn_scb_bd_test.json`
    - `NativQA_bn_scb_bd_train.json`
- `bangla_in/`
    - `NativQA_bn_scb_in_dev.json`
    - `NativQA_bn_scb_in_test.json`
    - `NativQA_bn_scb_in_train.json`
- `english_bd/`
    - `NativQA_en_NA_bd_dev.json`
    - `NativQA_en_NA_bd_test.json`
    - `NativQA_en_NA_bd_train.json`
- `english_qa/`
    - `NativQA_en_NA_qa_dev.json`
    - `NativQA_en_NA_qa_test.json`
    - `NativQA_en_NA_qa_train.json`
- `hindi_in/`
    - `NativQA_hi_NA_in_dev.json`
    - `NativQA_hi_NA_in_test.json`
    - `NativQA_hi_NA_in_train.json`
- `nepali_np/`
    - `NativQA_ne_NA_np_test.json`
- `turkish_tr/`
    - `NativQA_tr_NA_tr_dev.json`
    - `NativQA_tr_NA_tr_test.json`
    - `NativQA_tr_NA_tr_train.json`


#### Example of a data
```
{
    "data_id": "cf92ec1e52b4b3071d263a1063b43928",
    "category": "immigration",
    "input_query": "How long can you stay in Qatar on a visitors visa?",
    "question": "Can I extend my tourist visa in Qatar?",
    "is_reliable": "very_reliable",
    "answer": "If you would like to extend your visa, you will need to proceed to immigration headquarters in Doha prior to the expiry of your visa and apply there for an extension.",
    "source_answer_url": "https://hayya.qa/en/web/hayya/faq"
}
```
##### Field Descriptions:
- **`data_id`**: Unique identifier for each data entry.
- **`category`**: General topic or category of the query (e.g., "health", "religion").
- **`input_query`**: The original user-submitted query.
- **`question`**: The formalized question derived from the input query.
- **`is_reliable`**: Indicates the reliability of the provided answer (`"very_reliable"`, `"somewhat_reliable"`, `"unreliable"`).
- **`answer`**: The system-provided answer to the query.
- **`source_answer_url`**: URL of the source from which the answer was derived.


### Statistics
Distribution of the **MultiNativQA** dataset across different languages.
<p align="left"> <img src="./language_donut_chart.png" style="width: 60%;" id="title-icon"> </p>

This dataset consists of two types of data: annotated and un-annotated. We considered the un-annotated data as additional data. Please find the data statistics below:

Statistics of our **MultiNativQA** dataset including languages with the final annotated QA pairs from different location.

| Language    | City       | Train   | Dev   | Test   | Total  |
|-------------|------------|---------|-------|--------|--------|
| Arabic      | Doha       | 3,649   | 492   | 988    | 5,129  |
| Assamese    | Assam      | 1,131   | 157   | 545    | 1,833  |
| Bangla      | Dhaka      | 7,018   | 953   | 1,521  | 9,492  |
| Bangla      | Kolkata    | 6,891   | 930   | 2,146  | 9,967  |
| English     | Dhaka      | 4,761   | 656   | 1,113  | 6,530  |
| English     | Doha       | 8,212   | 1,164 | 2,322  | 11,698 |
| Hindi       | Delhi      | 9,288   | 1,286 | 2,745  | 13,319 |
| Nepali      | Kathmandu  | --      | --    | 561    | 561    |
| Turkish     | Istanbul   | 3,527   | 483   | 1,218  | 5,228  |
| **Total**   |            | **44,477** | **6,121** | **13,159** | **63,757** |



We provide the un-annotated additional data stats below:

| Language-Location 	     | # of QA 	     |
|-------------------------|---------------|
| Arabic-Egypt      	     | 7,956   	     |
| Arabic-Palestine  	     | 5,679   	     |
| Arabic-Sudan      	     | 4,718   	     |
| Arabic-Syria      	     | 11,288  	     |
| Arabic-Tunisia    	     | 14,789  	     |
| Arabic-Yemen      	     | 4,818   	     |
| English-New York  	     | 6,454   	     |
| **Total**             	 | **55,702**  	 |

### How to download data

```
import os
import json
from datasets import load_dataset


dataset_names = ['arabic_qa', 'assamese_in', 'bangla_bd', 'bangla_in', 'english_bd',
                'english_qa', 'hindi_in', 'nepali_np', 'turkish_tr']
base_dir="./MNQA/"

for dname in dataset_names:    
    output_dir = os.path.join(base_dir, dname)
    # load each language
    dataset = load_dataset("QCRI/MultiNativQA", name=dname)
    # Save the dataset to the specified directory. This will save all splits to the output directory.
    dataset.save_to_disk(output_dir)
    # iterate over splits to save the data into json format
    for split in ['train','dev','test']:
        data = []
        if split not in dataset:
            continue
        for idx, item in enumerate(dataset[split]):
            data.append(item)
        output_file = os.path.join(output_dir, f"{split}.json")
        with open(output_file, 'w', encoding='utf-8') as f:
            json.dump(data, f, ensure_ascii=False, indent=4)
```

### License
The dataset is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). The full license text can be found in the accompanying licenses_by-nc-sa_4.0_legalcode.txt file.

### Contact & Additional Information
For more details, please visit our [official website](http://nativqa.gitlab.io/).

### Citation
You can access the full paper [here](https://arxiv.org/pdf/2407.09823).

```
@article{hasan2024nativqa,
      title={NativQA: Multilingual Culturally-Aligned Natural Query for LLMs},
      author={Hasan, Md Arid and Hasanain, Maram and Ahmad, Fatema and Laskar, Sahinur Rahman and Upadhyay, Sunaya and Sukhadia, Vrunda N and Kutlu, Mucahid and Chowdhury, Shammur Absar and Alam, Firoj},
      journal={arXiv preprint arXiv:2407.09823},
      year={2024}
      publisher={arXiv:2407.09823},
      url={https://arxiv.org/abs/2407.09823},
}
```