# FAIR-ISLE: A Bilingual Platform for AI-Mediated Inclusive Rewriting and Critical Language Awareness

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Official repository for the paper: **"FAIR-ISLE: A Bilingual Platform for AI-mediated Inclusive Rewriting and Critical Language Awareness"**, accepted at the 12th Italian Conference on Computational Linguistics (**CLiC-it 2026**), Palermo, Italy.

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## 📌 Overview

**FAIR-ISLE** (*Female Artificial Intelligence Resource for a Safe Learning Environment*) is an interdisciplinary bilingual (Italian–English) platform designed to promote critical language awareness and identify/rewrite identity-based linguistic biases using Large Language Models (LLMs).

Unlike conventional grammar correctors, FAIR-ISLE integrates a **Human-in-the-Loop (HITL)** interaction mechanism to position users as active evaluators rather than passive recipients of automated corrections, fostering metalinguistic reflection and digital literacy in educational contexts.

### Key Features
- 🌐 **Bilingual Rewriting (Italian & English):** Handles English neutralization strategies as well as complex Italian morphosyntactic structures.
- 🎯 **Broad Bias Coverage:** Mitigates gender bias, cisnormative bias, ageism, racial bias, xenophobia, sexual-orientation bias, ableism, socioeconomic bias, and intersectional bias.
- ⚡ **Parameter-Efficient SFT:** Fine-tuned `Llama-3-8B-Instruct` using **Unsloth** and **LoRA** (4-bit NF4 quantization) targeting all linear layers.
- 🖥️ **Interactive Web Interface:** Lightweight Flask app providing sentence-level tokenization (`NLTK`), low-latency inference ($T=0.1$), and structured JSON feedback logging for DPO fine-tuning loops.

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## 📊 Dataset & Model Performance

### Dataset
- **Total Samples:** 10,391 parallel sentence pairs generated via teacher-student distillation (GPT-4.1) and validated by expert human annotators.
- **Split:** 90% Training / 10% Testing.
- Includes explicitly tagged neutral sentences to train identity mappings.

### In-Vitro Evaluation

| Model | sacreBLEU | chrF | METEOR | COMET |
| :--- | :---: | :---: | :---: | :---: |
| **Llama-3-8B-Instruct (LoRA FT)** | **82.42** | **87.47** | **0.83** | **0.91** |
| **mT5-base (Full FT)** | 75.00 | 80.19 | 0.73 | 0.86 |

### Real-World In-Vivo Evaluation (250 Undergraduate Students, 540 Samples)
- **User Acceptance Rate (UAR):** `77.4%` ( rewrites accepted without modification )
- **User Correction Rate (UCR):** `18.5%` ( manually edited by users )
- **Rejection Rate (RR):** `4.1%` ( dismissed suggestions )

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## 🛠️ Hyperparameter Configuration

| Parameter | Value |
| :--- | :--- |
| **Base Model** | Llama-3-8B-Instruct (Unsloth 4-bit NF4) |
| **Max Sequence Length** | 2048 |
| **LoRA Rank ($r$) / Alpha ($ lpha$)** | 16 / 16 |
| **Target Modules** | $q, k, v, o, gate, up, down$ |
| **Optimizer / LR** | AdamW (8-bit) / $2 	imes 10^{-4}$ (Linear Scheduler) |
| **Batch Size / Effective Batch Size** | 8 per device (Grad Accum = 2) $
ightarrow$ 16 |
| **Epochs** | 5 |

---

## 🚀 Quick Start

### 1. Installation

```bash
git clone https://github.com/uniba-nlp/FAIR-ISLE.git
cd FAIR-ISLE

conda create -n fair-isle python=3.10 -y
conda activate fair-isle

pip install -r requirements.txt
```

### 2. Running the Interactive Web Interface

Launch the Flask server to interact with the FAIR-ISLE Inclusive Rewriter:

```bash
python app.py --model_path uniba-nlp/Llama-3-8B-FAIR-ISLE --port 5000
```
Open your browser at `http://localhost:5000`.

### 3. Model Fine-Tuning with Unsloth

To replicate the training using LoRA fine-tuning:

```bash
python train.py \
    --base_model "unsloth/llama-3-8b-instruct-bnb-4bit" \
    --dataset_path "./data/fair_isle_dataset.json" \
    --output_dir "./models/fair-isle-lora" \
    --epochs 5 \
    --batch_size 8 \
    --learning_rate 2e-4
```

---

## 💡 Prompt Template

The fine-tuned model follows the Llama-3 instruction format:

```text
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an assistant skilled in inclusive language. Rewrite the sentence provided by the user to make it inclusive. If the sentence is already correct or neutral, return it unchanged.<|eot_id|>
<|start_header_id|>user<|end_header_id|>

Original sentence: {input_sentence}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

Rewritten sentence: {output_sentence}<|eot_id|>
```

---

## 📜 Citation

If you use FAIR-ISLE in your research or application, please cite our paper:

```bibtex
@inproceedings{filograsso2026fairisle,
  title     = {FAIR-ISLE: A Bilingual Platform for AI-mediated Inclusive Rewriting and Critical Language Awareness},
  author    = {Filograsso, Francesca and Siciliani, Lucia and Maglie, Rosita Belinda and Basile, Pierpaolo and de Gemmis, Marco},
  booktitle = {Proceedings of the Twelfth Italian Conference on Computational Linguistics (CLiC-it 2026)},
  year      = {2026},
  address   = {Palermo, Italy},
  publisher = {CEUR Workshop Proceedings}
}
```

---

## 👥 Authors & Affiliations

- **Francesca Filograsso**\* (`francesca.filograsso@uniba.it`) — Department of Education, Psychology and Communication Sciences, University of Bari Aldo Moro
- **Lucia Siciliani**\* (`lucia.siciliani@uniba.it`) — Department of Computer Science, University of Bari Aldo Moro
- **Rosita Belinda Maglie** (`rosita.maglie@uniba.it`) — Department of Education, Psychology and Communication Sciences, University of Bari Aldo Moro
- **Pierpaolo Basile** (`pierpaolo.basile@uniba.it`) — Department of Computer Science, University of Bari Aldo Moro
- **Marco de Gemmis** (`marco.degemmis@uniba.it`) — Department of Computer Science, University of Bari Aldo Moro

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## 📄 License

This project is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).