Update README.md

README.md

@@ -3,8 +3,169 @@ task_categories:
 - question-answering
 language:
 - en
-pretty_name: art
+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
+
+---
+