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---
license: mit
dataset_info:
features:
- name: id
dtype: string
- name: instruction
dtype: string
- name: question
dtype: string
- name: option1
dtype: string
- name: option2
dtype: string
- name: option3
dtype: string
- name: option4
dtype: string
- name: answer
dtype: string
- name: image
dtype: image
- name: audio
dtype: audio
splits:
- name: validation
num_bytes: 873288128.0
num_examples: 900
download_size: 819328629
dataset_size: 873288128.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Multi-TW: Traditional Chinese Language Learning Dataset
## Dataset Description
Multi-TW is a Traditional Chinese language learning and assessment dataset containing 900 multiple-choice questions with multimedia content. This dataset is designed for evaluating multi-modal language models on Traditional Chinese comprehension tasks.
## Dataset Structure
The dataset contains 900 samples in the validation split, suitable for benchmarking purposes.
### Data Fields
- `id`: Unique identifier for each question
- `instruction`: Task instructions in Chinese
- `question`: The question text in Chinese
- `option1`: Multiple choice option A
- `option2`: Multiple choice option B
- `option3`: Multiple choice option C
- `option4`: Multiple choice option D (may be empty)
- `answer`: Correct answer (A, B, C, or D)
- `image`: PIL Image object (for visual questions)
- `audio`: Audio data with sampling rate (for audio questions)
### Data Composition
- **Total samples**: 900
- **Samples with images**: 450
- **Samples with audio**: 450
- **Answer distribution**: A: 249, B: 261, C: 263, D: 127
- **Question types**: L (Listening): 660, R (Reading): 240
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("ntuai/multi-tw")
validation_data = dataset["validation"]
# Access a sample
sample = validation_data[0]
print(f"Question: {sample['question']}")
print(f"Options: {sample['option1']}, {sample['option2']}, {sample['option3']}")
print(f"Answer: {sample['answer']}")
# Check if sample has image or audio
if sample['image'] is not None:
# Process image
image = sample['image']
if sample['audio'] is not None:
# Process audio
audio_array = sample['audio']['array']
sampling_rate = sample['audio']['sampling_rate']
```
## Dataset Statistics
The dataset covers various aspects of Chinese language learning:
- **Visual comprehension**: Questions requiring image understanding
- **Audio comprehension**: Questions requiring audio understanding
- **Multiple choice format**: 3-4 options per question
- **Balanced distribution**: Relatively even distribution across answer choices
## License
本研究使用華測會官網之公開模擬試題，試題著作權為華測會所有，僅供個人學習使用，不得作為營利用途
## Citation
If you use this dataset in your research, please cite:
```bibtex
@dataset{multi_tw_2025,
title={Multi-TW: Benchmarking Multimodal Models on Traditional Chinese Question Answering in Taiwan},
author={Jui-Ming Yao, Bing-Cheng Xie, Sheng-Wei Peng, Hao-Yuan Chen, He-Rong Zheng, Bing-Jia Tan, Peter Shaojui Wang, and Shun-Feng Su},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/datasets/ntuai/multi-tw}
}
```
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