README.md

---
license: mit
dataset_info:
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: task1
    num_bytes: 100788
    num_examples: 250
  - name: task2
    num_bytes: 42363
    num_examples: 250
  - name: task3
    num_bytes: 67642
    num_examples: 250
  - name: task4
    num_bytes: 146014
    num_examples: 250
  - name: task5
    num_bytes: 22327
    num_examples: 100
  - name: task6
    num_bytes: 27509
    num_examples: 100
  download_size: 55342
  dataset_size: 406643
configs:
- config_name: default
  data_files:
  - split: task1
    path: data/task1-*
  - split: task2
    path: data/task2-*
  - split: task3
    path: data/task3-*
  - split: task4
    path: data/task4-*
  - split: task5
    path: data/task5-*
  - split: task6
    path: data/task6-*
---
# TutorQA Benchmark

This dataset is part of the benchmark introduced in the paper [Graphusion: Leveraging Large Language Models for
Scientific Knowledge Graph Fusion and Construction in NLP Education](https://arxiv.org/pdf/2407.10794v1). We also release more data in our [GitHub page](https://github.com/IreneZihuiLi/Graphusion/tree/main).
It contains 6 tasks designed for evaluating various aspects of reasoning, graph understanding, and language generation.

## Dataset Structure

Each task is a separate split:
- `task1`: Relation Judgment
- `task2`: Prerequisite Prediction
- `task3`: Path Searching
- `task4`: Subgraph Completion
- `task5`: Clustering
- `task6`: Idea Hamster (no answers, open ended)

| Split  | Fields                     |
|:-------|:----------------------------|
| task1  | `question`, `answer`         |
| task2  | `question`, `answer`         |
| task3  | `question`, `answer`         |
| task4  | `question`, `answer`         |
| task5  | `question`, `answer`         |
| task6  | `question`                   |

## Usage Example

```python
from datasets import load_dataset

dataset = load_dataset("li-lab/tutorqa")

# Access individual tasks
task1 = dataset["task1"]
task6 = dataset["task6"]
```


## Citation

```bibtex
@inproceedings{yang2025graphusion,
  title={Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective},
  author={Yang, Rui and Yang, Boming and Feng, Aosong and Ouyang, Sixun and Blum, Moritz and She, Tianwei and Jiang, Yuang and Lecue, Freddy and Lu, Jinghui and Li, Irene},
  booktitle={Proceedings of the NLP4KGC Workshop at The Web Conference 2025 (WWW'25)},
  year={2025},
  url={https://arxiv.org/abs/2410.17600}
}