---
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}
}
```