Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
License:
File size: 4,924 Bytes
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task_categories:
- question-answering
language:
- en
pretty_name: Argument Reasoning Tasks (ART)
tags:
- reasoning
- llm_evaluation
- argument-mining
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# 🧠 Argument Reasoning Tasks (ART) Dataset
**Evaluating natural language argumentative reasoning in large language models.**
---
## 📖 Overview
The **Argument Reasoning Tasks (ART)** dataset is a **large-scale benchmark** designed to evaluate the ability of large language models (LLMs) to perform **natural language argumentative reasoning**.
It contains **multiple-choice questions** where models must identify missing argument components, given an argument context and reasoning structure.
---
## 🧩 Argumentation Structures
ART covers **16 task types** derived from four core argumentation structures:
1. **Serial reasoning** – chained inference steps.
2. **Linked reasoning** – multiple premises jointly supporting a conclusion.
3. **Convergent reasoning** – independent premises supporting a conclusion.
4. **Divergent reasoning** – a single premise leading to multiple possible conclusions.
---
## 📄 Source & Reference
This dataset was introduced in:
> **Debela Gemechu, Ramon Ruiz-Dolz, Henrike Beyer, and Chris Reed. 2025.**
> *Natural Language Reasoning in Large Language Models: Analysis and Evaluation.*
> Findings of the Association for Computational Linguistics: ACL 2025, pp. 3717–3741.
> Vienna, Austria: Association for Computational Linguistics.
> [📄 Read the paper](https://aclanthology.org/2025.findings-acl.192/) | DOI: [10.18653/v1/2025.findings-acl.192](https://doi.org/10.18653/v1/2025.findings-acl.192)
```bibtex
@inproceedings{gemechu-etal-2025-natural,
title = {Natural Language Reasoning in Large Language Models: Analysis and Evaluation},
author = {Gemechu, Debela and Ruiz-Dolz, Ramon and Beyer, Henrike and Reed, Chris},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
pages = {3717--3741},
year = {2025},
address = {Vienna, Austria},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.findings-acl.192/},
doi = {10.18653/v1/2025.findings-acl.192}
}
````
---
## 📂 Dataset Details
* **Hugging Face repo:** [debela-arg/art](https://huggingface.co/datasets/debela-arg/art)
* **License:** CC BY-NC-SA 4.0 (non-commercial, share alike)
* **Languages:** English
* **Domain:** Argumentative reasoning, question answering
* **File format:** JSON
* **Size:** \~482 MB
* **Splits:** Single `train` split with **88,628 examples**
---
### 🗂 Example JSON Entry
```json
{
"prompt": "Please answer the following multiple-choice question...",
"task_type": "1H-C",
"answer": ["just one of three children returning to school..."],
"data_source": "qt30"
}
```
**Fields:**
* `prompt` – Question with context and multiple-choice options
* `task_type` – Argument reasoning task category
* `answer` – Correct answer(s)
* `data_source` – Original source corpus
---
## 📊 Statistics
| Attribute | Value |
| -------------- | -------------------------------------------- |
| Total examples | 88,628 |
| Task types | 16 |
| Data sources | MTC, AAEC, CDCP, ACSP, AbstRCT, US2016, QT30 |
---
## ⚡ How to Load the Dataset
Install the dependencies:
```bash
pip install datasets pandas
```
Load in Python:
```python
from datasets import load_dataset
import pandas as pd
# Load the train split
dataset = load_dataset("debela-arg/art", split="train")
# Convert to DataFrame
df = pd.DataFrame(dataset)
print("Total examples:", len(df))
print("Available columns:", df.columns.tolist())
print("Task type distribution:")
print(df["task_type"].value_counts())
```
---
## 🔍 Suggested Uses
* **LLM evaluation** – Benchmark reasoning capabilities
* **Few-shot prompting** – Create reasoning-based examples for instruction tuning
* **Error analysis** – Identify reasoning failure modes in models
---
## 📌 Citation
If you use ART in your work, please cite:
```bibtex
@inproceedings{gemechu-etal-2025-natural,
title = {Natural Language Reasoning in Large Language Models: Analysis and Evaluation},
author = {Gemechu, Debela and Ruiz-Dolz, Ramon and Beyer, Henrike and Reed, Chris},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
pages = {3717--3741},
year = {2025},
address = {Vienna, Austria},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.findings-acl.192/},
doi = {10.18653/v1/2025.findings-acl.192}
}
```
---
## 🛠 Maintainers
* **Author:** Debela Gemechu, Ramon Ruiz-Dolz, Henrike Beyer and Chris Reed
---
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