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---
# Arabic Legal Judgment Prediction Dataset
## Overview
This dataset is designed for **Arabic Legal Judgment Prediction (LJP)**, collected and preprocessed from **Saudi commercial court judgments**. It serves as a benchmark for evaluating Large Language Models (LLMs) in the legal domain, particularly in low-resource settings.
The dataset is released as part of our research:
> **Can Large Language Models Predict the Outcome of Judicial Decisions?**
> *Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, and Amani Al-Ghraibah*
> [arXiv:2501.09768](https://arxiv.org/abs/2501.09768)
## Dataset Details
- **Size:** 3752 training samples, 538 test samples.
- **Annotations:** 75 diverse Arabic instructions generated using GPT-4o, varying in length and complexity.
- **Tasks Supported:**
- Zero-shot, One-shot, and Fine-tuning evaluation of Arabic legal text understanding.
## Data Structure
The dataset is provided in a structured format:
```python
from datasets import load_dataset
dataset = load_dataset("mbayan/Arabic-LJP")
print(dataset)
```
The dataset contains:
- **train**: Training set with 3752 samples
- **test**: Test set with 538 samples
Each sample includes:
- **Input text:** Legal case description
- **Target text:** Judicial decision
## Benchmark Results
We evaluated the dataset using **LLaMA-based models** with different configurations. Below is a summary of our findings:
| **Metric** | **LLaMA-3.2-3B** | **LLaMA-3.1-8B** | **LLaMA-3.2-3B-1S** | **LLaMA-3.2-3B-FT** | **LLaMA-3.1-8B-FT** |
|--------------------------|------------------|------------------|---------------------|---------------------|---------------------|
| **Coherence** | 2.69 | 5.49 | 4.52 | *6.60* | **6.94** |
| **Brevity** | 1.99 | 4.30 | 3.76 | *5.87* | **6.27** |
| **Legal Language** | 3.66 | 6.69 | 5.18 | *7.48* | **7.73** |
| **Faithfulness** | 3.00 | 5.99 | 4.00 | *6.08* | **6.42** |
| **Clarity** | 2.90 | 5.79 | 4.99 | *7.90* | **8.17** |
| **Consistency** | 3.04 | 5.93 | 5.14 | *8.47* | **8.65** |
| **Avg. Qualitative Score**| 3.01 | 5.89 | 4.66 | *7.13* | **7.44** |
| **ROUGE-1** | 0.08 | 0.12 | 0.29 | *0.50* | **0.53** |
| **ROUGE-2** | 0.02 | 0.04 | 0.19 | *0.39* | **0.41** |
| **BLEU** | 0.01 | 0.02 | 0.11 | *0.24* | **0.26** |
| **BERT** | 0.54 | 0.58 | 0.64 | *0.74* | **0.76** |
**Caption**: A comparative analysis of performance across different LLaMA models. The model names have been abbreviated for simplicity: **LLaMA-3.2-3B-Instruct** is represented as LLaMA-3.2-3B, **LLaMA-3.1-8B-Instruct** as LLaMA-3.1-8B, **LLaMA-3.2-3B-Instruct-1-Shot** as LLaMA-3.2-3B-1S, **LLaMA-3.2-3B-Instruct-Finetuned** as LLaMA-3.2-3B-FT, and **LLaMA-3.1-8B-Finetuned** as LLaMA-3.1-8B-FT.
### **Key Findings**
- Fine-tuned smaller models (**LLaMA-3.2-3B-FT**) achieve performance **comparable to larger models** (LLaMA-3.1-8B).
- Instruction-tuned models with one-shot prompting (LLaMA-3.2-3B-1S) significantly improve over zero-shot settings.
- Fine-tuning leads to a noticeable boost in **coherence, clarity, and faithfulness** of predictions.
## Usage
To use the dataset in your research, load it as follows:
```python
from datasets import load_dataset
dataset = load_dataset("mbayan/Arabic-LJP")
# Access train and test splits
train_data = dataset["train"]
test_data = dataset["test"]
```
## Repository & Implementation
The full implementation, including preprocessing scripts and model training code, is available in our GitHub repository:
🔗 **[GitHub](https://github.com/MohamedBayan/Arabic-Legal-Judgment-Prediction)**
## Citation
If you use this dataset, please cite our work:
```
@misc{kmainasi2025largelanguagemodelspredict,
title={Can Large Language Models Predict the Outcome of Judicial Decisions?},
author={Mohamed Bayan Kmainasi and Ali Ezzat Shahroor and Amani Al-Ghraibah},
year={2025},
eprint={2501.09768},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.09768},
}
```
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