---
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

---